Annals of Internal Medicine最新文献

Background: Despite the scale of prostate-specific antigen (PSA) testing for prostate cancer (PCa) screening, prediction models do not predict time-to-event end points or adjust for patient life expectancy.

Objective: To develop, externally validate, and compare to existing tools a novel prognostic model for risk for prostate cancer-specific mortality (PCSM) after a PSA test.

Design: Prognostic model development in the PCa screening group of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial; external validation in a Veterans Affairs (VA) population of patients undergoing PSA testing.

Setting: United States. PLCO patients were enrolled from 1993 to 2001, and VA patients underwent PSA testing from 2002 to 2006. Survival follow-up was updated through 2022 in both cohorts.

Patients: Male patients aged 55 to 74 years in the PLCO PCa screening group (n = 33 339) and the VA Healthcare System (n = 174 787).

Measurements: The model's predicted outcome is PCSM at a specified time point; predictors included PSA level, family history of PCa, and race. Predictors of other-cause mortality included age; body mass index; smoking status; and presence of hypertension, diabetes, or stroke.

Results: In the model development cohort, the area under the receiver operating characteristic curve (AUC) at 29.5 years from screening was 0.666 compared with 0.643 for a previously validated prostate biopsy risk model (Prostate Biopsy Collaborative Group [PBCG]) (P < 0.001). In the external validation cohort, the AUC at 20 years from screening was 0.776 for the PLCO model versus 0.749 for the PBCG model (P = 0.031).

Limitation: The model may not be generalizable to more contemporary PSA screening practices given the periods studied.

Conclusion: This PCSM prognostic model was developed from long-term clinical trial data, was externally validated in a large national cohort, and may be used to improve interpretation of PSA results.

Primary funding source: None.

Background: The effects of sodium-glucose cotransporter-2 inhibitors (SGLT-2is) on diabetic foot disease have been mixed in prior trials of SGLT-2is compared with placebo. The comparative risk for diabetic foot disease with SGLT-2is compared with glucagon-like peptide-1 receptor agonists (GLP-1RAs) is unknown.

Objective: To compare risks for foot disease in new users of SGLT-2is and GLP-1RAs.

Design: Cohort study using target trial emulation.

Setting: Danish population-based study.

Participants: Patients with type 2 diabetes initiating SGLT-2i or GLP-1RA treatment, identified using national health care registry data from 2013 to 2023, and a convenience sample enrolled in a research cohort study with additional behavioral and clinical assessments.

Measurements: Incident diagnosis of foot disease outcomes (peripheral neuropathy, peripheral artery disease, foot ulcers, or lower-limb amputation) as defined by the International Working Group on the Diabetic Foot. Inverse probability of treatment-weighted risk ratios (RRs) were estimated, with adjustment for 45 demographic, clinical, and other factors.

Results: The registry cohort included 53 769 new users of SGLT-2is and 30 380 of GLP-1RAs. During 6 years of follow-up, any foot disease occurred in 10.8% of SGLT-2i users and 12.0% of GLP-1RA users, corresponding to an RR of 0.90 (95% CI, 0.84 to 0.97) in an intention-to-treat analysis; differences did not emerge until after year 3, when 40% of SGLT-2i users and 32% of GLP-1RA users had discontinued initial treatment. The modest reduction in risk among SGLT-2i users was driven by lower risk for neuropathy (RR, 0.78 [CI, 0.68 to 0.87]). Users of SGLT-2is and GLP-1RAs had similar risks for peripheral artery disease, foot ulcers, amputations, and all-cause mortality.

Limitation: Residual confounding; exposure and outcome misclassification.

Conclusion: New SGLT-2i users had a modestly lower risk for foot disease largely driven by a lower risk for neuropathy than GLP-1RA users.

Primary funding source: Aarhus University and Center for Population Medicine.

Background: Hospital-based violence intervention programs (HVIPs) are widespread, but their effectiveness for violence prevention is unclear.

Objective: To determine the effects of Boston Medical Center's HVIP on future violence outcomes among violently injured young adults.

Design: Target trial emulation using observational data.

Setting: Boston, Massachusetts.

Participants: Young adults aged 16 to 34 years who survived a shooting or stabbing.

Intervention: Target trials of 2 treatment strategies using the same eligibility criteria, time zero, and control group were emulated: 1) any treatment: engaging with the HVIP within 1 month of injury and 2) sustained treatment: initiating within 1 month and engaging more than 4 of the first 8 weeks.

Measurements: Combined measure of violent reinjury or violence perpetration at 1, 2, and 3 years, using hospital and police data.

Results: 1328 patients met criteria; 565 (42.5%) initiated within 1 month. Of these, 58 (10.2%) sustained engagement. In the any-treatment analysis, estimated cumulative incidence was roughly equal between the treatment and control strategies at 1, 2, and 3 years. In the sustained engagement analysis, treatment was associated with considerably lower cumulative incidence (4.5% [95% CI, 1.1% to 9.3%] at 1 year; 5.1% [CI, 1.1% to 9.3%] at 2 years; 6.4% [CI, 1.4% to 12.9%] at 3 years) versus the control strategy (8.7% [CI, 6.6% to 10.0%] at 1 year; 12.3% [CI, 10.2% to 14.5%] at 2 years; 14.3% [CI, 11.8% to 16.6%] at 3 years), with corresponding risk reductions of 47.6% (CI, -19.8% to 86.7%), 58.5% (CI, 21.6% to 91.2%), and 55.3% (CI, 4.9% to 90.2%). Confidence intervals were wide.

Limitation: Despite our target trial emulation approach, results could be confounded by unmeasured factors associated with program engagement.

Conclusion: Although HVIPs can improve long-term violence outcomes, these effects seem to require intensive participant engagement.

Primary funding source: Fund for a Safer Future.

Clinical impact ratings: GIM/FP/GP: [Formula: see text] Public Health: [Formula: see text].

Clinical impact ratings: GIM/FP/GP: [Formula: see text] Cardiology: [Formula: see text].