Induced membrane technique versus one-stage autografting in management of atrophic nonunion of long bone in the lower limb: clinical and health burden outcomes

OBJECTIVE

In this study, we aimed to compare the outcomes of the two-stage induced membrane technique (IMT) and one-stage autografting in the treatment of aseptic atrophic nonunion in lower limb long bones.

METHODS

From January 2014 to January 2022, we reviewed all surgically treated long bone nonunion patients, including patients aged 18 years or older with atrophic nonunion, who were either treated with the two-stage induced membrane technique (IMT) or one-stage autografting. Outcome parameters interns of clinical, quality of life and healthcare burden were recorded and retrospectively analysed between the two treatment populations. The follow-up time was at least 1 year.

RESULTS

In total, 103 patients who met the criteria for aseptic atrophic nonunion were enrolled. Among them, 41 (39.8%) patients were treated with two-stage IMT, and 62 (60.2%) patients were treated with one-stage autologous bone grafting. The follow-up time was 12 to 68 months, with an average of 28.4 months. The bone healing rate was comparable in both groups (IMT: 92.7% vs. one-stage grafting: 91.9%, P = 0.089) at 12 months post-operation, and the bone healing Lane-Sandhu score was superior in the IMT group (mean: 8.68 vs. 7.81, P = 0.002). Meanwhile, the SF-12 scores of subjective physical component score (PCS) (mean: 21.36 vs. 49.64, P < 0.01) and mental health component score (MCS) (mean: 24.85 vs. 46.14, P < 0.01) significantly increased in the IMT group, as well as in the one-stage grafting group, and no statistically significant difference was found within groups. However, the total hospital stays (median: 8 days vs. 14 days, P < 0.01) and direct medical healthcare costs (median: ¥30,432 vs. ¥56,327, P < 0.05) were greater in the IMT group, while the complications (nonunion 8, infection 3, material failure 2, and donor site pain 6) were not significantly different between the two groups (17.1% vs. 19.4, P = 0.770).

CONCLUSION

The data indicate that two-stage method of IMT serves as an alternative method in treating atrophic nonunion; however, it may not be a preferred option, in comprehensive considering patient clinical outcomes and healthcare burden. More evidence-based research is needed to further guide clinical decision-making.

A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials

In heart failure (HF) trials efficacy is usually assessed by a composite endpoint including cardiovascular death (CVD) and heart failure hospitalizations (HFHs), which has traditionally been evaluated with a time-to-first-event analysis based on a Cox model. As a considerable fraction of events is ignored that way, methods for recurrent events were suggested, among others the semiparametric proportional rates models by Lin, Wei, Yang, and Ying (LWYY model) and Mao and Lin (Mao-Lin model). In our work we apply least false parameter theory to explain the behavior of the composite treatment effect estimates resulting from the Cox model, the LWYY model, and the Mao-Lin model in clinically relevant scenarios parameterized through joint frailty models. These account for both different treatment effects on the two outcomes (CVD, HFHs) and the positive correlation between their risk rates. For the important setting of beneficial outcome-specific treatment effects we show that the correlation results in composite treatment effect estimates, which are decreasing with trial duration. The estimate from the Cox model is affected more by the attenuation than the estimates from the recurrent event models, which both demonstrate very similar behavior. Since the Mao-Lin model turns out to be less sensitive to harmful effects on mortality, we conclude that, among the three investigated approaches, the LWYY model is the most appropriate one for the composite endpoint in HF trials. Our investigations are motivated and compared with empirical results from the PARADIGM-HF trial (ClinicalTrials.gov identifier: NCT01035255), a large multicenter trial including 8399 chronic HF patients.

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Relapse- and Immunosuppression-Free Survival after Hematopoietic Stem Cell Transplantation: How Can We Assess Treatment Success for Complex Time-to-Event Endpoints?

In most clinical oncology trials, time-to-first-event analyses are used for efficacy assessment, which often do not capture the entire disease process. Instead, the focus may be on more complex time-to-event endpoints, such as the course of disease after the first event or endpoints occurring after randomization. We propose "relapse- and immunosuppression-free survival" (RIFS) as an innovative and clinically relevant outcome measure for assessing treatment success after hematopoietic stem cell transplant (SCT). To capture the time-dynamic relationship of multiple episodes of immunosuppressive therapy during follow-up, relapse, and nonrelapse mortality, a multistate model was developed. The statistical complexity is that the probability of RIFS is nonmonotonic over time; thus, standard time-to-first-event methodology is inappropriate for formal treatment comparisons. Instead, a generalization of the Kaplan-Meier method was used for probability estimation, and simulation-based resampling was suggested as a strategy for statistical inference. We reanalyzed data from a recently published phase III trial in 201 leukemia patients after SCT. The study evaluated long-term treatment success of standard graft-versus-host disease prophylaxis plus a pretransplant antihuman T-lymphocyte immunoglobulin compared with standard prophylaxis alone. Results suggested that treatment increased the long-term probability of RIFS by approximately 30% during the entire follow-up period, which complements the original findings. This article highlights the importance of complex endpoints in oncology, which provide deeper insight into the treatment and disease process over time. Multistate models combined with resampling are highlighted as a promising tool to evaluate treatment success beyond standard endpoints. An example code is provided in the Supplementary Materials.