Association of Lifestyle Intervention With Risk for Cardiovascular Events Differs by Level of Glycated Hemoglobin

PURPOSE

We reevaluated the Action for Health in Diabetes (Look AHEAD) intensive lifestyle intervention (ILI) to assess whether the effect of ILI on cardiovascular disease (CVD) prevention differed by baseline glycated hemoglobin (HbA1c).

METHODS

Look AHEAD randomized 5145 adults, aged 45 to 76 years with type 2 diabetes and overweight/obesity to ILI or a diabetes support and education (DSE) control group for a median of 9.6 years. ILI focused on achieving weight loss through decreased caloric intake and increased physical activity. We assessed the parent trial's primary composite CVD outcome. We evaluated additive and multiplicative heterogeneity of the intervention on CVD risk by baseline HbA1c.

RESULTS

Mean baseline HbA1c was 7.3% (SD 1.2) and ranged from 4.4% (quintile 1) to 14.5% (quintile 5). We observed additive and multiplicative heterogeneity of the association between ILI and CVD (all P < .001) by baseline HbA1c. Randomization to ILI was associated with lower CVD risk for HbA1c quintiles 1 [hazard ratio (HR): 0.68, 95% confidence interval (CI): 0.53, 0.88] and 2 (HR: 0.80, 95% CI: 0.66, 0.96) and associated with higher CVD risk for HbA1c quintile 5 (HR: 1.27, 95% CI: 1.02, 1.58), compared to DSE.

CONCLUSION

Among adults with type 2 diabetes and overweight/obesity, randomization to a lifestyle intervention was differentially associated with CVD risk by baseline HbA1c such that it was associated with lower risk at lower HbA1c levels and higher risk at higher HbA1c levels. There is a critical need to develop and tailor lifestyle interventions to be successful for individuals with type 2 diabetes and high HbA1c.

Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group

BACKGROUND

Machine learning (ML) has been applied to an increasing number of predictive problems in laboratory medicine, and published work to date suggests that it has tremendous potential for clinical applications. However, a number of groups have noted the potential pitfalls associated with this work, particularly if certain details of the development and validation pipelines are not carefully controlled.

METHODS

To address these pitfalls and other specific challenges when applying machine learning in a laboratory medicine setting, a working group of the International Federation for Clinical Chemistry and Laboratory Medicine was convened to provide a guidance document for this domain.

RESULTS

This manuscript represents consensus recommendations for best practices from that committee, with the goal of improving the quality of developed and published ML models designed for use in clinical laboratories.

CONCLUSIONS

The committee believes that implementation of these best practices will improve the quality and reproducibility of machine learning utilized in laboratory medicine.

SUMMARY

We have provided our consensus assessment of a number of important practices that are required to ensure that valid, reproducible machine learning (ML) models can be applied to address operational and diagnostic questions in the clinical laboratory. These practices span all phases of model development, from problem formulation through predictive implementation. Although it is not possible to exhaustively discuss every potential pitfall in ML workflows, we believe that our current guidelines capture best practices for avoiding the most common and potentially dangerous errors in this important emerging field.

Integrated Infectious Disease and Substance Use Disorder Care for the Treatment of Injection Drug Use-Associated Infections: A Prospective Cohort Study With Historical Control

Background

To address the infectious disease (ID) and substance use disorder (SUD) syndemic, we developed an integrated ID/SUD clinical team rooted in harm reduction at a county hospital in Miami, Florida. The Severe Injection-Related Infection (SIRI) team treats people who inject drugs (PWID) and provides medical care, SUD treatment, and patient navigation during hospitalization and after hospital discharge. We assessed the impact of the SIRI team on ID and SUD treatment and healthcare utilization outcomes.

Methods

We prospectively collected data on patients seen by the SIRI team. A diagnostic code algorithm confirmed by chart review was used to identify a historical control group of patients with SIRI hospitalizations in the year preceding implementation of the SIRI team. The primary outcome was death or readmission within 90 days post-hospital discharge. Secondary outcomes included initiation of medications for opioid use disorder (MOUD) and antibiotic course completion.

Results

There were 129 patients included in the study: 59 in the SIRI team intervention and 70 in the pre-SIRI team control group. SIRI team patients had a 45% risk reduction (aRR, 0.55 [95% confidence interval CI, .32-.95]; 24% vs 44%) of being readmitted in 90 days or dying compared to pre-SIRI historical controls. SIRI team patients were more likely to initiate MOUD in the hospital (93% vs 33%, P < .01), complete antibiotic treatment (90% vs 60%, P < .01), and less likely to have patient-directed discharge (17% vs 37%, P = .02).

Conclusions

An integrated ID/SUD team was associated with improvements in healthcare utilization, MOUD initiation, and antibiotic completion for PWID with infections.

Association of maternal polygenic risk scores for mental illness with perinatal risk factors for offspring mental illness

We examined whether genetic risk for mental illness is associated with known perinatal risk factors for offspring mental illness to determine whether gene-environmental correlation might account for the associations of perinatal factors with mental illness. Among 8983 women with 19,733 pregnancies, we found that genetic risk for mental illness was associated with any smoking during pregnancy [attention-deficit hyperactivity disorder (ADHD) and overall genetic risk], breast-feeding for less than 1 month (ADHD, depression, and overall genetic risk), experience of intimate partner violence in the year before the birth (depression and overall genetic risk), and pregestational overweight or obesity (bipolar disorder). These results indicate that genetic risk may partly account for the association between perinatal conditions and mental illness in offspring.