Studying the Outcomes in Patients with Tricuspid Regurgitation Treated with Valve Repair or Valve Replacement: Interpreting the Survival Pattern on The Long Term by Application of Artificial Intelligence Methods.

Background: The reconstruction of individual patient data from published Kaplan-Meier survival curves is a new technique (often denoted as the IPDfromKM method) for studying efficacy in cases where multiple trials are available, and the endpoint is long-term mortality. In patients with tricuspid regurgitation, both valve repair and valve replacement have been proposed to improve prognosis; 6 controlled clinical trials (CTs) have been conducted to compare the two therapeutic options mentioned above. The objective of our analysis was to study these six trials through the application of the IPDfromKM method. Methods: In the present report, we applied the IPDfromKM method to carry out a pooled analysis of these 6 CTs to investigate the effectiveness of valve repair vs valve replacement and to assess the between-study heterogeneity from this clinical material. After reconstructing individual patient data from these 6 trials, patients treated with valve repair were pooled together and their Kaplan-Meier curve was generated. Likewise, patients treated with valve replacement were pooled together and their Kaplan-Meier curve was generated. Finally, these two curves were compared by standard survival statistics. The hazard ratio (HR) was determined; death from any cause was the endpoint. Results: These 6 CTs included a total of 552 patients; in each of these CTs, the patient group treated with valve repair was compared with another group treated with valve replacement. Our statistical results showed a significantly better survival for valve repair compared with valve replacement (HR, 0.6098; 95% confidence intervals (CI), 0.445 to 0.835; p = 0.002). Heterogeneity was found to be significant in the 6 patient arms undergoing replacement, but not in those undergoing valve repair. In valve replacement, the classification of patients in class III or IV of New York Heart Association (NYHA) was the main negative prognostic factor. Conclusions: Our analysis confirmed the methodological advantages of the IPDfromKM method in the indirect comparative analysis of multiple trials. These advantages include appropriate analysis of censored patients, original assessment of heterogeneity, and graphical presentation of the results, wherein individual patients retain an important role.

Advancing autologous CAR T-cell therapy through real-time patient health data integration: a simulation-based approach.

Autologous chimeric antigen receptor T-cell therapy presents promising treatment outcomes for various cancers. However, its potential is restrained by unique supply chain challenges, including dynamic patient health conditions and extended turnaround time. These challenges often lead to missed optimal treatment windows, impeding the effective delivery of life-saving treatments. This article presents SimPAC (simulation-based decision support for Patient-centric manufacturing of autologous cell therapies). SimPAC is designed to model and incorporate real-time patient health conditions into the supply chain decisions of autologous chimeric antigen receptor T-cell therapy. SimPAC integrates system dynamics and agent-based simulation techniques, facilitating the adaptation of manufacturing processes and production schedules based on real-time patient health conditions. SimPAC can model various patient disease progressions using parametric functions, nonparametric functions, or tabular data. Additionally, SimPAC offers easy configuration options to model various cell therapy supply chains. We provide two case studies to demonstrate the capabilities of SimPAC and highlight the benefits of patient-centric manufacturing, including improved survival rates and potential economic advantages. However, while the benefits are significant, our study also emphasizes the importance of balancing improved patient outcomes, economic viability and ethical considerations in the context of personalized medicine. SimPAC can be used to explore applications of this approach to diverse therapeutic contexts and supply chain configurations.

Association of symptom severity and cerebrospinal fluid alterations in recent onset psychosis in schizophrenia-spectrum disorders - An individual patient data meta-analysis.

Neuroinflammation and blood-cerebrospinal fluid barrier (BCB) disruption could be key elements in schizophrenia-spectrum disorders(SSDs) etiology and symptom modulation. We present the largest two-stage individual patient data (IPD) meta-analysis, investigating the association of BCB disruption and cerebrospinal fluid (CSF) alterations with symptom severity in first-episode psychosis (FEP) and recent onset psychotic disorder (ROP) individuals, with a focus on sex-related differences. Data was collected from PubMed and EMBASE databases. FEP, ROP and high-risk syndromes for psychosis IPD were included if routine basic CSF-diagnostics were reported. Risk of bias of the included studies was evaluated. Random-effects meta-analyses and mixed-effects linear regression models were employed to assess the impact of BCB alterations on symptom severity. Published (6 studies) and unpublished IPD from n = 531 individuals was included in the analyses. CSF was altered in 38.8 % of individuals. No significant differences in symptom severity were found between individuals with and without CSF alterations (SMD = -0.17, 95 %CI -0.55-0.22, p = 0.341). However, males with elevated CSF/serum albumin ratios or any CSF alteration had significantly higher positive symptom scores than those without alterations (SMD = 0.34, 95 %CI 0.05-0.64, p = 0.037 and SMD = 0.29, 95 %CI 0.17-0.41p = 0.005, respectively). Mixed-effects and simple regression models showed no association (p > 0.1) between CSF parameters and symptomatic outcomes. No interaction between sex and CSF parameters was found (p > 0.1). BCB disruption appears highly prevalent in early psychosis and could be involved in positive symptoms severity in males, indicating potential difficult-to-treat states. This work highlights the need for considering BCB breakdownand sex-related differences in SSDs clinical trials and treatment strategies.

Influence of donor sex and age on graft outcome in kidney transplantation.

BACKGROUND: There is a known recipient sex-dependent association between donor sex and kidney transplant survival. We hypothesized that donor age also modifies the association between donor sex and graft survival. METHODS: First, deceased donor kidney transplant recipients (1988-2019, n = 461 364) recorded in the Scientific Registry of Transplant Recipients, the Australia and New Zealand Dialysis and Transplant Registry and the Collaborative Transplant Study were analyzed. We used multivariable Cox regression models to estimate the association between donor sex and death censored graft loss, accounting for the modifying effects of recipient sex and donor age; donor age was categorized as 5-19, 20-34, 35-49, 50-59 and ≥60 years. Results from cohort-specific Cox models were combined using individual patient data meta-analysis. RESULTS: Among female recipients of donors aged <60 years, graft loss hazards did not differ by donor sex; recipients of female donors ≥60 years showed significantly lower graft loss hazards than recipients of male donors of the same age [combined adjusted hazard ratio (aHR) 0.90, 95% CI 0.86-0.94]. Among male recipients, female donors aged <50 years were associated with significantly higher graft loss hazards than same-aged male donors (5-19 years: aHR 1.11, 95% CI 1.02-1.21; 20-34 years: aHR 1.08, 95% CI 1.02-1.15; 35-49 years: aHR 1.07, 95% CI 1.04-1.10). There were no significant differences in graft loss by donor sex among male recipients of donors aged ≥50 years. CONCLUSION: Donor age modifies the association between donor sex and graft survival. Older female donors were associated with similar or lower hazards of graft failure than older male donors in both male and female recipients, suggesting a better functional reserve of older female donor kidneys.

Two-stage targeted maximum likelihood estimation for mixed aggregate and individual participant data analysis with an application to multidrug resistant tuberculosis.

In this study, we develop a new method for the meta-analysis of mixed aggregate data (AD) and individual participant data (IPD). The method is an adaptation of inverse probability weighted targeted maximum likelihood estimation (IPW-TMLE), which was initially proposed for two-stage sampled data. Our methods are motivated by a systematic review investigating treatment effectiveness for multidrug resistant tuberculosis (MDR-TB) where the available data include IPD from some studies but only AD from others. One complication in this application is that participants with MDR-TB are typically treated with multiple antimicrobial agents where many such medications were not observed in all studies considered in the meta-analysis. We focus here on the estimation of the expected potential outcome while intervening on a specific medication but not intervening on any others. Our method involves the implementation of a TMLE that transports the estimation from studies where the treatment is observed to the full target population. A second weighting component adjusts for the studies with missing (inaccessible) IPD. We demonstrate the properties of the proposed method and contrast it with alternative approaches in a simulation study. We finally apply this method to estimate treatment effectiveness in the MDR-TB case study.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. RESULTS: The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.

Estimating individualized treatment effects using an individual participant data meta-analysis.

BACKGROUND: One key aspect of personalized medicine is to identify individuals who benefit from an intervention. Some approaches have been developed to estimate individualized treatment effects (ITE) with a single randomized control trial (RCT) or observational data, but they are often underpowered for the ITE estimation. Using individual participant data meta-analyses (IPD-MA) might solve this problem. Few studies have investigated how to develop risk prediction models with IPD-MA, and it remains unclear how to combine those methods with approaches used for ITE estimation. In this article, we compared different approaches using both simulated and real data with binary and time-to-event outcomes to estimate the individualized treatment effects from an IPD-MA in a one-stage approach. METHODS: We compared five one-stage models: naive model (NA), random intercept (RI), stratified intercept (SI), rank-1 (R1), and fully stratified (FS), built with two different strategies, the S-learner and the T-learner constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions, and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo ( N = 40 237 , 836 events). RESULTS: Simulation results showed that using the S-learner led to better ITE estimation performances for both binary and time-to-event outcomes. None of the risk models stand out and had significantly better results. For the INDANA dataset with a binary outcome, the naive and the random intercept models had the best performances. CONCLUSIONS: For the choice of the strategy, using interactions with treatment (the S-learner) is preferable. For the choice of the method, no approach is better than the other.

Digital health interventions for postoperative recovery in children: a systematic review.

BACKGROUND: Digital health interventions offer a promising approach for monitoring during postoperative recovery. However, the effectiveness of these interventions remains poorly understood, particularly in children. The objective of this study was to assess the efficacy of digital health interventions for postoperative recovery in children. METHODS: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the use of automation tools for searching and screening. We searched five electronic databases for randomised controlled trials or non-randomised studies of interventions that utilised digital health interventions to monitor postoperative recovery in children. The study quality was assessed using Cochrane Collaboration's Risk of Bias tools. The systematic review protocol was prospectively registered with PROSPERO (CRD42022351492). RESULTS: The review included 16 studies involving 2728 participants from six countries. Tonsillectomy was the most common surgery and smartphone apps (WeChat) were the most commonly used digital health interventions. Digital health interventions resulted in significant improvements in parental knowledge about the child's condition and satisfaction regarding perioperative instructions (standard mean difference=2.16, 95% confidence interval 1.45-2.87; z=5.98, P<0.001; I2=88%). However, there was no significant effect on children's pain intensity (standard mean difference=0.09, 95% confidence interval -0.95 to 1.12; z=0.16, P=0.87; I2=98%). CONCLUSIONS: Digital health interventions hold promise for improving parental postoperative knowledge and satisfaction. However, more research is needed for child-centric interventions with validated outcome measures. Future work should focus development and testing of user-friendly digital apps and wearables to ease the healthcare burden and improve outcomes for children. SYSTEMATIC REVIEW PROTOCOL: PROSPERO (CRD42022351492).

Dropout from exercise trials among cancer survivors-An individual patient data meta-analysis from the POLARIS study.

INTRODUCTION: The number of randomized controlled trials (RCTs) investigating the effects of exercise among cancer survivors has increased in recent years; however, participants dropping out of the trials are rarely described. The objective of the present study was to assess which combinations of participant and exercise program characteristics were associated with dropout from the exercise arms of RCTs among cancer survivors. METHODS: This study used data collected in the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study, an international database of RCTs investigating the effects of exercise among cancer survivors. Thirty-four exercise trials, with a total of 2467 patients without metastatic disease randomized to an exercise arm were included. Harmonized studies included a pre and a posttest, and participants were classified as dropouts when missing all assessments at the post-intervention test. Subgroups were identified with a conditional inference tree. RESULTS: Overall, 9.6% of the participants dropped out. Five subgroups were identified in the conditional inference tree based on four significant associations with dropout. Most dropout was observed for participants with BMI >28.4 kg/m2 , performing supervised resistance or unsupervised mixed exercise (19.8% dropout) or had low-medium education and performed aerobic or supervised mixed exercise (13.5%). The lowest dropout was found for participants with BMI >28.4 kg/m2 and high education performing aerobic or supervised mixed exercise (5.1%), and participants with BMI ≤28.4 kg/m2 exercising during (5.2%) or post (9.5%) treatment. CONCLUSIONS: There are several systematic differences between cancer survivors completing and dropping out from exercise trials, possibly affecting the external validity of exercise effects.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping.

BACKGROUND: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. OBJECTIVE: This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. METHODS: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. RESULTS: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. CONCLUSIONS: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.

BACKGROUND: Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data. METHODS: A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH). RESULTS: The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788). CONCLUSIONS: In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.

Evaluating Health Care Professionals' Readiness for e-Health Adoption in the Context of the COVID-19 Pandemic: A Georgian Perspective.

Background: The COVID-19 pandemic has accelerated the adoption of Electronic health (e-Health), leveraging technologies such as telemedicine, electronic health records, artificial intelligence, and patient engagement platforms. This transformation underscores e-Health's role in providing efficient, patient-centered care. Our study explores health care professionals' readiness for these technologies, emphasizing the need for tailored education in this evolving landscape. Methods: In our study, conducted between February and March 2023, we administered a questionnaire-based survey to 500 staff members (82.4% female, 17.6% male) aged 25-70 from medical universities in Tbilisi, Georgia. The structured questionnaire covered topics such as computer literacy, telemedicine awareness, patient data security, and ethical considerations. We employed SPSS v21.0 for data analysis, encompassing descriptive statistics and thematic analysis of open-ended responses. Results: Our study included 500 participants categorized into five age groups. Notably, 31% considered themselves computer "experts," while 69% rated their skills as "intermediate" or "advanced." Furthermore, 85% used computers professionally, with 33% having practical computer training. Interestingly, 59% expressed interest in information technology training. Regarding e-Health, 15% believed it involves remote communication between health care professionals and patients, while 42% considered it "correct," and 37% "might be correct." Concerning its application in managing patients, opinions varied. In terms of e-Health's integration into Georgia's health care, responses ranged. Regarding patient data safety, participants exhibited diverse views. Finally, opinions on the necessity of informed consent for e-Health applications varied among participants. Conclusions: Our study explores health care professionals' readiness for e-Health adoption during the COVID-19 pandemic. It reveals varying computer literacy levels, a willingness to learn, differing views on e-Health applications, and mixed opinions on its integration into Georgian health care. These findings emphasize the need for clear e-Health terminology, education, tailored approaches, and a focus on data privacy and informed consent. Overall, e-Health's transformative role in modern health care is underscored.

Hepatocellular carcinoma risk in patients with chronic hepatitis B receiving tenofovir- vs. entecavir-based regimens: Individual patient data meta-analysis.

BACKGROUND & AIMS: The comparative risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) receiving tenofovir disoproxil fumarate (TDF) vs. entecavir (ETV) remains controversial. In this individual patient data (IPD) meta-analysis, we aimed to compare HCC risk between the two drugs and identify subgroups who may benefit more from one treatment than the other. METHODS: Published meta-analyses, electronic databases and congress proceedings were searched to identify eligible studies through January 2021. We compared HCC risk between the two drugs using a multivariable Cox proportional hazards model with anonymised IPD from treatment-naïve patients with CHB receiving TDF or ETV for ≥1 year. Treatment effect consistency was explored in propensity score matching (PSM), weighting (PSW) and subgroup analyses for age, sex, hepatitis B e-antigen (HBeAg) positivity, cirrhosis and diabetes status. RESULTS: We included 11 studies from Korea, Taiwan and Hong Kong involving 42,939 patients receiving TDF (n = 6,979) or ETV (n = 35,960) monotherapy. Patients receiving TDF had significantly lower HCC risk (adjusted hazard ratio [HR] 0.77; 95% CI 0.61-0.98; p = 0.03). Lower HCC risk with TDF was consistently observed in PSM (HR 0.73; 95% CI 0.59-0.88; p <0.01) and PSW (HR 0.83; 95% CI 0.67-1.03; p = 0.10) analyses and in all subgroups, with statistical significance in the ≥50 years of age (HR 0.76; 95% CI 0.58-1.00; p <0.05), male (HR 0.74; 95% CI 0.58-0.96; p = 0.02), HBeAg-positive (HR 0.69; 95% CI 0.49-0.97; p = 0.03) and non-diabetic (HR 0.79; 95% CI 0.63-1.00; p <0.05) subgroups. CONCLUSION: TDF was associated with significantly lower HCC risk than ETV in patients with CHB, particularly those with HBeAg positivity. Longer follow-up may be needed to better define incidence differences between the treatments in various subgroups. IMPACT AND IMPLICATIONS: Previous aggregate data meta-analyses have reported inconsistent conclusions on the relative effectiveness of tenofovir disoproxil fumarate and entecavir in reducing hepatocellular carcinoma risk in patients with chronic hepatitis B (CHB). This individual patient data meta-analysis on 11 studies involving 42,939 patients from Korea, Taiwan and Hong Kong suggested that tenofovir disoproxil fumarate-treated patients have a significantly lower hepatocellular carcinoma risk than entecavir-treated patients, which was observed in all subgroups of clinical interest and by different analytical methodologies. These findings should be taken into account by healthcare providers when determining the optimal course of treatment for patients with CHB and may be considered in ensuring that treatment guidelines for CHB remain pertinent.

Structure-preserving integrated analysis for risk stratification with application to cancer staging.

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. A well-known example is the American Joint Committee on Cancer staging for cancer that uses tumor size, node involvement, and metastasis status. We consider a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. We build on the "lasso-tree" method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns. In addition, the parametrization of the lasso-tree method makes it very natural to incorporate the underlying order information in the risk factors. In this article, we also strengthen the lasso-tree method by establishing its theoretical properties for which Lin and others (2013. Lasso tree for cancer staging with survival data. Biostatistics 14, 327-339) did not pursue. We evaluate our method in extensive simulation studies and an analysis of multiple breast cancer data sets.

Prognostic value of circulating tumor DNA in operable non-small cell lung cancer: a systematic review and reconstructed individual patient-data based meta-analysis.

BACKGROUND: This reconstructed individual patient data (IPD)-based meta-analysis is aimed to summarize the current findings and comprehensively investigate the predictive value of circulating tumor DNA (ctDNA) in operable non-small cell lung cancer (NSCLC). METHODS: PubMed, Cochrane and Embase were searched to include potentially eligible studies. The primary outcomes included progression-free survival (DFS) by ctDNA status at baseline, postoperative, and longitudinal timepoints. The IPD-based survival data was retracted and used in reconstructed IPD-based meta-analysis. Subgroup analysis was implemented based on the baseline characteristics. RESULTS: Totally, 28 studies were involved, including 15 full-length articles (1686 patients) for IPD-based synthesis and 20 studies for conventional meta-analysis. The IPD-based meta-analysis discovered that patients with positive ctDNA status at the baseline (hazard ratio, HR = 3.73, 95% confidential interval, CI: 2.95-4.72), postoperative (3.96, 2.19-7.16), or longitudinal timepoints (12.33, 8.72-17.43) showed significantly higher risk of recurrence. Patients with persistent ctDNA-negative status had the lowest recurrence rate, and the negative conversion of ctDNA from baseline to postoperative timepoints was correlated with elevated DFS. Subgroup analyses suggested that stage II-III patients with ctDNA-positive status may achieve preferable therapeutic outcomes. CONCLUSIONS: Plasm ctDNA monitoring shows excellent clinical significance at the tested timepoints. Perioperative conversion of ctDNA status may indicate the therapeutic effect of radical surgery. Postoperative adjuvant therapy may be determined according to the ctDNA status. TRAIL REGISTRATION: CRD42022304445.

Optimising plasma clozapine levels to improve treatment response: an individual patient data meta-analysis and receiver operating characteristic curve analysis.

BACKGROUND: Although clozapine is the most efficacious medication for treatment-refractory schizophrenia, not all patients will have an adequate response. Optimising clozapine dose using therapeutic drug monitoring could therefore maximise response. AIMS: Using individual patient data, we undertook a receiver operating characteristic (ROC) curve analysis to determine an optimal therapeutic range for clozapine levels to guide clinical practice. METHOD: We conducted a systematic review of PubMed, PsycINFO and Embase for studies that provided individual participant level data on clozapine levels and response. These data were analysed using ROC curves to determine the prediction performance of plasma clozapine levels for treatment response. RESULTS: We included data on 294 individual participants from nine studies. ROC analysis yielded an area under the curve of 0.612. The clozapine level at the point of optimal diagnostic benefit was 372 ng/mL; at this level, the response sensitivity was 57.3%, and specificity 65.7%. The interquartile range for treatment response was 223-558 ng/mL. There was no improvement in ROC performance with mixed models including patient gender, age or length of trial. Clozapine dose and clozapine concentration to dose ratio did not provide significantly meaningful prediction of response to clozapine. CONCLUSIONS: Clozapine dose should be optimised based on clozapine therapeutic levels. We found that a range between 250 and 550 ng/mL could be recommended, while noting that a level of >350 ng/mL is the most optimal for response. Although some patients may not respond without clozapine levels >550 ng/mL, the benefits should be weighed against the increased risk of adverse drug reactions.

Direct evidence gap on fixed versus adjusted-dose benznidazole for adults with chronic Chagas disease without cardiomyopathy: Systematic review and individual patient data meta-analysis.

OBJECTIVES: To determine the comparative efficacy and safety of a fixed dose of benznidazole (BZN) with an adjusted-dose for Trypanosoma cruzi-seropositive adults without cardiomyopathy. METHODS: We conducted a systematic review and individual participant data (IPD) meta-analysis following Cochrane methods, and the PRISMA-IPD statement for reporting. Randomised controlled trials (RCTs) allocating participants to fixed or adjusted doses of BZN for T. cruzi-seropositive adults without cardiomyopathy were included. We searched (December 2021) Cochrane, MEDLINE, EMBASE, LILACS and trial registries and contacted Chagas experts. Selection, data extraction, risk of bias assessment using the Cochrane tool, and a GRADE summary of finding tables were performed independently by pairs of reviewers. We conducted a random-effects IPD meta-analysis using the one-stage strategy, or, if that was impossible, the two-stage strategy. RESULTS: Five RCTs (1198 patients) were included, none directly comparing fixed with adjusted doses of BZN. Compared to placebo, BZN therapy was strongly associated with negative qPCR and sustainable parasitological clearance regardless of the type of dose and subgroup analysed. For negative qPCR, the fixed/adjusted rate of odds ratios (RORF/A ) was 8.83 (95% CI 1.02-76.48); for sustained parasitological clearance, it was 4.60 (95% CI 0.40-52.51), probably indicating at least non-inferior effect of fixed doses, with no statistically significant interactions by scheme for global and most subgroup estimations. The RORF/A for treatment interruption due to adverse events was 0.44 (95% CI 0.14-1.38), probably indicating no worse tolerance of fixed doses. CONCLUSIONS: We found no direct comparison between fixed and adjusted doses of BZN. However, fixed doses versus placebo are probably not inferior to weight-adjusted doses of BZN versus placebo in terms of parasitological efficacy and safety. Network IPD meta-analysis, through indirect comparisons, may well provide the best possible answers in the near future. REGISTRATION: The study protocol was registered in PROSPERO (CRD42019120905).

Reimagining Patient Data Access for Researchers.

OBJECTIVES: Widespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients' privacy rights. METHODS: We analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients' data aggregators, and final aggregation across patients' data. RESULTS: Creating new HEOR data systems requires new organizations and funding, while also protecting patients' data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients' data. New secondary data aggregators must combine individuals' aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients' data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. CONCLUSIONS: Important steps remain uncompleted to achieve maximally available HEOR data while protecting patients' privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people's health.

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.

BACKGROUND: Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. METHODS: We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. RESULTS: The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. CONCLUSION: This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

Headache clinicians' perspectives on the remote monitoring of patients' electronic diary data: A qualitative study.

OBJECTIVE: We assessed headache clinicians' viewpoints on potential remote access to patients' digital headache diary data and the practicalities of data utilization. BACKGROUND: With the ubiquitous nature of electronic medical records and the existence of remote monitoring (RM) for many medical conditions, there is now the potential for remote symptom monitoring for patients with headache disorders. While patients are asked to utilize headache diaries, clinicians may or may not have access to the data before patient visits, and their perspectives regarding this emerging technology are currently unknown. METHODS: After recruiting participants from the National Institutes of Health Pain Consortium Network, the American Headache Society Special Interest Section listservs, and Twitter and Facebook social media platforms, we conducted 20 semi-structured qualitative interviews of headache providers across the United States from various types of institutions and asked them their perspectives on remote access to patient headache diary data. We transcribed the interviews, which were then coded by two independent coders. Themes and sub-themes were developed using inductive content analysis. RESULTS: All clinicians felt the RM data needed to be integrated into the electronic medical record. Six themes emerged from the interviews: (i) Clinician perspectives on how RM could be beneficial but at other times could create obstacles/challenges, (ii) operationally, data integration could benefit headache care, (iii) there should be initial logistical considerations for bringing RM into clinical care, (iv) education may need to be provided to both patients and clinicians, (v) there are likely research benefits associated with RM, and (vi) additional suggestions for considering potential integration of RM into practice. CONCLUSIONS: While headache clinicians had mixed opinions on the benefits/challenges that RM presents to patient care, patient satisfaction, and visit time, new ideas emerged that may help advance the field.