Carter, Rickey E.

INTRODUCTION: The SPEC-AI Nigeria trial (NCT05438576) was designed to use artificial intelligence (AI) to screen for pregnancy-related cardiomyopathy in the peripartum period. Using data from this study, we evaluated the utility of AI-predicted delta age (adjusted AI-predicted age − chronological age) as a surrogate for biological age. METHODS: We included 1,187 pregnant and postpartum women enrolled between August 2022 and September 2023 with follow-up through May 2024. Standard 12-lead electrocardiograms (ECGs) were recorded at study entry to generate AI age predictions. Artificial intelligence-predicted age was adjusted using estimated reference ranges obtained from a community-dwelling cohort of 25,144 individuals with AI-ECG age estimated and documented chronological age. Logistic and Cox-proportional hazards regression were used to examine associations with comorbid cardiovascular conditions and maternal mortality, respectively. RESULTS: Adjusted AI-predicted age was significantly higher among women with any cardiovascular condition, peripartum cardiomyopathy, or who died within 18 months (7, 14, and 23 years older, respectively) compared to those without these conditions who had values similar to the normal reference ranges (adjusted AI-predicted age difference less than 1 year). Artificial intelligence-predicted delta age greater than the 75th percentile was associated with an odds ratio (OR) of 2.06 for any cardiovascular condition, OR of 4.98 for left ventricular systolic dysfunction, and a hazard ratio of 32.81 for all-cause mortality; all values of P<.001. CONCLUSIONS/IMPLICATIONS: Artificial intelligence-ECG-derived biological age appears to be a potentially useful measure of cardiovascular health status and risk among pregnant and postpartum women. However, its role in monitoring maternal health requires further exploration.

Abstract Context Higher gluconeogenesis (GNG) contributes to higher nocturnal endogenous glucose production (EGP) in type 2 diabetes (T2D). Studies using 13C magnetic resonance spectroscopy (MRS) have confirmed lower hepatic glycogen content in subjects with T2D than in subjects with no diabetes (ND). Objective We determined the role of glycogen loading (GL) vs nonglycogen loading (NGL) on the contribution of GNG to nocturnal EGP in T2D. Methods In total, 14 subjects with T2D and 15 matched subjects with ND were studied on 2 occasions, with GL (60% carbohydrate) vs NGL (40% carbohydrate) isocaloric meals for 3 days, in random order in the overnight state. [6,6-2H2] glucose was infused to measure EGP, deuterium labelled water was used to measure GNG, and 13C MRS scans were performed in fed and fasted states to measure hepatic glycogen content. Results Hepatic glycogen content and nocturnal EGP were higher (P &lt; .05) in GL vs NGL in both cohorts. The % GNG to EGP averaged ∼50% in subjects with ND throughout the night after both meals. In contrast, % GNG to nocturnal EGP in T2D was lower with GL vs NGL and matched the pattern observed in subjects with ND with GL lowering overnight rates of GNG in subjects with T2D. Conclusion Selective targeting of GNG at night with appropriate medications could reduce nocturnal and early morning fasting hyperglycemia and hepatic insulin resistance in people with T2D.

BACKGROUND: Plasma NT-proBNP (N-terminal pro-B-type natriuretic peptide) is commonly used to diagnose heart failure with preserved ejection fraction (HFpEF), but its diagnostic performance in the ambulatory/outpatient setting is unknown because previous studies lacked objective reference standards. METHODS: Among patients with chronic dyspnea, diagnosis of HFpEF or noncardiac dyspnea was determined conclusively by exercise catheterization in a derivation cohort (n=414), multicenter validation cohort 1 (n=560), validation cohort 2 (n=207), and a nonobese Japanese validation cohort 3 (n=77). Optimal NT-proBNP cut points for HFpEF rule out (optimizing sensitivity) and rule in (optimizing specificity) were derived and tested, stratified by obesity and atrial fibrillation. Derived cut points were tested in 3 additional validation cohorts (cohorts 4–6) in whom HFpEF was diagnosed by resting catheterization only (n=260), previous hospitalization for heart failure (n=447), or exercise echocardiography (n=517), respectively. RESULTS: Current recommended rule-out NT-proBNP threshold <125 pg/mL had 82% sensitivity (95% CI, 77%–88%) with a body mass index (BMI) <35 kg/m 2 , decreasing to 67% (95% CI, 58%–77%) with a BMI ≥35 kg/m 2 . A lower rule-out NT-proBNP threshold <50 pg/mL displayed good sensitivity with a BMI <35 kg/m 2 (97% [95% CI, 95%–99%]), with a modest decline in sensitivity with a BMI ≥35 kg/m 2 (86% [95% CI, 79%–93%]); diagnostic thresholds were confirmed in validation cohorts 1 and 2 (91% [95% CI, 88%–95%] and 86% [95% CI, 80%–93%] with a BMI <35 kg/m 2 ; 80% [95% CI, 74%–87%] and 84% [95% CI, 74%–93%] with a BMI ≥35 kg/m 2 ). Current consensus age- and BMI-stratified rule-in thresholds demonstrated only 65% specificity (95% CI, 57%–72%). Rule-in NT-proBNP threshold ≥500 pg/mL had 85% specificity (95% CI, 78%–91%) with a BMI <35 kg/m 2 (87% [95% CI, 80%–94%] and 90% [95% CI, 81%–99%] in validation cohorts), with 100% specificity at a BMI ≥35 kg/m 2 (93% [95% CI, 81%–100%] and 100% in validation cohorts). With a BMI ≥35 kg/m 2 , lower rule-in thresholds (≥220 pg/mL) provided good specificity (88% [95% CI, 73%–100%]; 93% [95% CI, 81%–100%] and 100% in validation cohorts). Findings were consistent in validation cohorts 3 through 6 (sensitivity of <50 pg/mL, 93%–98%; specificity of ≥500 pg/mL, 82%–89%). NT-proBNP provided no incremental discrimination among patients with history of AF; ≥98% of patients with AF and dyspnea were found to have HFpEF in our cohorts. CONCLUSIONS: In patients with chronic unexplained dyspnea, current rule-in and rule-out NT-proBNP diagnostic thresholds lead to unacceptably high error rates, with important interactions by obesity and AF status. In our study, NT-proBNP provided little value in those with AF and dyspnea because the presence of AF is by itself a robust biomarker of HFpEF. Use of separate rule-in and rule-out diagnostic thresholds stratified by BMI reduces miscategorization and can guide more appropriate use of exercise testing for possible HFpEF.

Background: Effective pain management is crucial for both comfort and outcomes, yet predicting and managing this pain is difficult. This study aimed to analyze postoperative pain in patients undergoing hand surgery at the Mayo Clinic Florida, examining how patient characteristics and anxiety affect pain outcomes. Methods: We conducted a single-arm clinical trial at Mayo Clinic Florida, recruiting patients undergoing hand surgery. Preoperative pain and anxiety were assessed using the Pain Catastrophizing Scale (PCS) and State-Trait Anxiety Inventory (STAI). Postoperatively, patients used an iPhone app to record pain levels and medication use every four hours. Devices were collected three days after surgery. We analyzed the relationship between demographics, PCS, STAI scores, and pain levels using linear and logistic regression models. All statistical tests were two-sided with significance set at p < 0.05, analyzed with R4.2.2. Results: Data were collected from 62 patients (62.9% women, 37.1% men) undergoing hand surgery. Participants were mainly White (90.3%), with 50% being in the middle-aged adult group. Most had low anxiety levels (80.6% STAI-S, 82.3% STAI-T) and low catastrophizing (61.3% PCS). Postoperative pain was low, with median scores between 1.0 and 2.0 over three days. Demographics, anxiety, and catastrophizing were not significant predictors of pain levels. Logistic regression showed time as a significant factor, with pain levels peaking on Day 3. Conclusions: Postoperative pain after hand surgery was generally low, with time being a significant predictor of increased pain. Demographic factors, anxiety, and catastrophizing did not significantly affect pain levels. Pain management should emphasize time-sensitive interventions and ongoing monitoring.

Abstract Background We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction. Methods We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test. Results In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed. Of these, 92% of kidney stones were eligible for AI-assisted interpretation. Out of these AI-eligible stones, 45% were able to be auto-released by the AI-augmented test system without human secondary review. Furthermore, the new AI-augmented kidney stone test system resulted in an apparent 40% reduction in incorrect laboratory results. Additionally, the new AI-augmented test system improved laboratory efficiency by 20%, improved staff satisfaction, and reduced the average analysis cost per kidney stone by $0.23. Conclusions The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.

Abstract: Background: Allergic contact dermatitis is a common, pruritic, debilitating skin disease, affecting at least 20% of the population. Objective: To prospectively validate a computer vision algorithm across all Fitzpatrick skin types. Methods: Each participant was exposed to 10 allergens. The reference criterion was obtained 5 days after initial patch placement by a board-certified dermatologist. The algorithm processed photographs of the test site obtained on Day 5. Human performance in reading the photographs was also evaluated. Results: A total of 206 evaluable participants [mean age 39 years, 66% (136/206) female, and 47% with Fitzpatrick skin types IV–VI] completed testing. Forty-two percent (87/206) of participants experienced 1 or more allergic reaction resulting in a total of 132 allergic reactions. The model provided high discrimination (AUROC 0.86, 95% CI: 0.82–0.90) and specificity (93%, 95% CI: 92%–94%) but with lower sensitivity (58%, 95% CI: 49%–67%). Human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers. There were no serious adverse events. Conclusions: The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample.

In the Spring of 2020, the United States of America (USA) deployed COVID-19 convalescent plasma (CCP) to treat hospitalized patients. Over 500,000 patients were treated with CCP during the first year of the pandemic. In this study, we estimated the number of actual inpatient lives saved by CCP treatment in the United States of America based on CCP weekly use, weekly national mortality data, and CCP mortality reduction data from meta-analyses of randomized controlled trials and real-world data. We also estimate the potential number of lives saved if CCP had been deployed for 100% of hospitalized patients or used in 15 to 75% of outpatients. Depending on the assumptions modeled in stratified analyses, we estimated that CCP saved between 16,476 and 66,296 lives. The CCP ideal use might have saved as many as 234,869 lives and prevented 1,136,133 hospitalizations. CCP deployment was a successful strategy for ameliorating the impact of the COVID-19 pandemic in the USA. This experience has important implications for convalescent plasma use in future infectious disease emergencies.