Background: The purpose of this study was to test the hypothesis that COVID‐19 status increases the incidence of new hypertension (HTN) and chronic kidney disease (CKD) in patients with Type 2 diabetes (T2D). Methods: This retrospective study consisted of 46,448 patients with T2D from the Montefiore Health System in the Bronx (3/01/2020–7/01/2023), of which 13,801 had a positive COVID‐19 test on record. Contemporary controls included those hospitalized for other lower respiratory tract infections (LRTIs) ( n = 1638) and nonhospitalized patients without COVID‐19 or LRTI ( n = 31009). Outcomes were assessed at follow‐up (2 months to 3 years) relative to baseline. Adjusted hazards ratios (aHRs) with 95% confidence interval (CI) were computed. Results: The cumulative incidences of HTN (22.32% vs. 9.13%, p < 0.001) and CKD (6.20% vs. 2.03%, p 0.05). Nonhospitalized COVID‐19 patients had higher risk of developing HTN compared to non‐COVID patients during all follow‐up (aHR 1.99, 95% CI [1.54, 2.57], p < 0.001), but hospitalized COVID‐19 patients had similar risk of developing HTN relative to patients hospitalized for LRTI (aHR 1.26 [0.70, 2.27], p = 0.441). Nonhospitalized COVID‐19 patients had higher risk of developing CKD compared to non‐COVID patients during all follow‐up (aHR 2.09 [1.69, 2.76], p < 0.001), but hospitalized COVID‐19 patients had similar risk of developing CKD relative to patients hospitalized for LRTI (aHR 0.96 [0.79, 1.36], p = 0.131). Conclusions: T2D patients with COVID‐19 were at higher risk of developing new disorders compared with COVID‐19‐negative controls and were at similar risk compared with those hospitalized for other LRTIs.

Context: Thyroid disease has been reported as a complication associated with acute SARS-CoV-2 infection. Objective: This study evaluated the long-term risk of incident thyroid dysfunction after COVID-19 in patients without preexisting thyroid disease. Methods: Patients without prior thyroid disease were grouped into 15 372 individuals hospitalized with COVID-19, 41 757 nonhospitalized individuals with COVID-19, 343 348 contemporary COVID-negative controls (March 1, 2020-August 17, 2024), and 171 141 historical controls (January 1, 2016-December 31, 2019) within the Montefiore Health System. Outcomes included new-onset thyroid disease, hypothyroidism, hyperthyroidism, and other/unspecified thyroid disease, and thyrotropin (TSH), free 3,5,3′-triiodothyronine (FT3), and free thyroxine (FT4) levels. The primary analysis used Cox regression with inverse probability weighting (IPW). Sensitivity analyses included multivariate adjustment, 1:1 propensity score matching, and comparisons with historical controls. Results: Compared to contemporary COVID-negative controls, patients hospitalized for COVID-19 had significantly higher risk of all thyroid disease (IPW-adjusted hazard ratio (HR) = 2.11 [2.03-2.19]), hypothyroidism (HR = 2.02 [1.92-2.12]), hyperthyroidism (HR = 2.31 [2.13-2.50]), and other/unspecified thyroid disease (HR = 2.27 [2.12-2.43]). Nonhospitalized COVID-positive patients also had increased risk of all thyroid disease (HR = 1.40 [1.35-1.46]), hypothyroidism (HR = 1.37 [1.31-1.43]), hyperthyroidism (HR = 1.56 [1.45-1.68]), and other/unspecified thyroid disease (HR = 1.42 [1.34-1.51]). Sensitivity analyses were consistent with the main findings. Estimates of risks were attenuated in the nonhospitalized cohort when compared to historical controls. Conclusion: SARS-CoV-2 infection is associated with an increased risk of all forms of new-onset thyroid disease investigated both in hospitalized and nonhospitalized patients. These findings suggest that enhanced screening for thyroid dysfunction may be warranted among individuals recovering from COVID-19.

Background/Objectives: To investigate the incidence of new-onset diabetic retinopathy (DR) in individuals with pre-existing type 2 diabetes (T2D) up to 3 years post SARS-CoV-2 infection. Methods: This retrospective study consisted of 5151 COVID-19 and 5151 propensity-matched non-COVID-19 patients with T2D in the Montefiore Health System between 1 March 2020 and 17 January 2023. The primary outcome was new-onset DR at least 2 months after the index date up to 17 January 2023. Matching for index date between groups was also used to ensure the same follow-up duration. Hazard ratios (HRs) were computed, adjusted for competing risks. Results: T2D patients with COVID-19 had a higher cumulative incidence of DR than T2D patients. The unadjusted HR for COVID-19 status for developing new DR was 2.44 [1.60, 3.73], p < 0.001. The adjusted HR was 1.70 [1.08, 2.70], p < 0.05, and the adjusted HR for prior insulin use was 3.28 [2.10, 5.12], p < 0.001. Sex, ethnicity, and major comorbidities had no significant association with outcome. Conclusions: T2D patients who contracted COVID-19 exhibited a significantly higher risk of developing DR within three years post infection compared to propensity-matched controls. The increased incidence was primarily driven by greater pre-existing insulin usage and SARS-CoV-2 infection in the COVID-19 positive cohort.

Photo ID 315013029 © Zimmytws | Dreamstime.com Introduction The US Supreme Court’s 2022 decision to overturn Roe v. Wade sparked widespread scrutiny due to its far-reaching legal, medical, and societal consequences. In the immediate aftermath, numerous states enacted or enforced abortion bans that triggered the rapid closure of abortion clinics. These closures have led many patients to travel long distances to obtain care, often encountering significant delays that disproportionately burden low-income individuals, rural populations, and racial and ethnic minority groups.[1] Yet focusing primarily on access — as much of the public health literature and advocacy research has done — obscures another significant long-term impact of Dobbs: the introduction of legal uncertainty into routine clinical decision-making.[2] Legal scholars and clinicians began documenting these risks immediately after the decision was issued.[3] This form of uncertainty reflects the ambiguity in how statutory restrictions are interpreted and applied in clinical settings. In states with abortion restrictions, physicians must consider potential legal ramifications with each medical decision, often under extreme, high-pressure conditions and with limited information. This has reshaped how care is delivered across a variety of clinical settings, so the health consequences of the Dobbs decision cannot be fully understood by focusing solely on abortion and miscarriage. Researchers and clinicians increasingly report that abortion restrictions have destabilized standard medical practice beyond access to abortion itself.[4] Bans have complicated the evidence-based management of miscarriages and obstetric emergencies, delayed or denied fertility treatments, constrained cancer therapies, and introduced uncertainty into the prescribing of medications with teratogenic or abortifacient potential, including methotrexate, misoprostol, and mifepristone.[5][6] Abortion restrictions have affected multiple aspects of standard health-care delivery, creating uncertainty for patients and clinicians. Delays Due to a Need for Legal Advice As a result, clinicians may seek legal counsel before making decisions.[7] However, much of the existing medical literature has focused on disruptions in abortion care rather than on how legal ambiguity affects routine clinical practice. Delays in treatment are a well-established source of harm, particularly in obstetric and emergency settings.[8] When clinicians must delay intervention until conditions worsen, patients face higher risks of infection, hemorrhage, and long-term morbidity and mortality. Investigative reporting and case analyses have documented instances in which doctors turned away patients seeking emergency medical care for a miscarriage multiple times, even as some patients developed severe sepsis.[9] Emerging evidence indicates that legal uncertainty is already associated with worse clinical outcomes. A study of Texas hospitals following the implementation of Senate Bill 8, a near-total ban on abortions after approximately six weeks of gestation, reported delays and denials of care for patients with pregnancy complications, including in instances where abortion was the standard of care.[10] A subsequent analysis found that Texas abortion restrictions delayed care and significantly increased maternal morbidity.[11] These findings raise concerns that the state laws restricting abortion in the aftermath of the Dobbs decision may be influencing patient outcomes. Qualitative research further illustrates the impacts. This new form of defensive practice, termed “hesitant medicine,” occurs when providers delay, withhold, or modify care to avoid legal scrutiny.[12] Physicians described prioritizing legal defensibility alongside clinical judgment in urgent scenarios. Legal ambiguity in other restrictive states has produced similar patterns.[13] Crucially, this phenomenon extends beyond abortion treatment. Emergency departments, inpatient services, outpatient clinics, and specialty practices all exist within the same legal environment. A lack of legal clarity can influence a multitude of related decisions (e.g., management of miscarriage, ectopic pregnancy, sepsis, cancer treatment). In a multi-state qualitative study, clinicians reported delaying or avoiding first-line, teratogenic therapies for reproductive-age patients who were not pregnant due to concern that a subsequent pregnancy could require abortion.[14] These effects extend to wanted pregnancies and clinical care in other specialties. Contribution to Physician Shortages There are also broader implications for the capacity of health systems nationwide. Whether through limiting the scope of practice, declining to provide certain services, or leaving restrictive areas altogether, legal threats are accelerating existing workforce and infrastructure challenges. A national analysis showed reductions in residency programs in states with abortion bans following the Dobbs decision, raising concerns about future access to care in such areas.[15] Reports also suggest that some physicians have left or are considering leaving restrictive states, potentially further exacerbating workforce shortages. These shortages may exacerbate pre-existing gaps in care in the most vulnerable areas. Conclusion Ultimately, the uncertainty about criminal liability impacts clinical practice. The constant need to analyze legal risks in routine, everyday decision-making produces downstream consequences that extend well beyond abortion care. Research focused solely on reduced access to abortion complements research on the legal uncertainty itself and its impact on many areas of clinical care. While some researchers and organizations focus on access to abortion care, it is crucial also to consider the impact of a fear of liability on clinical care beyond abortion, and even beyond pregnancy. Laws governing reproductive care that do not...

Background Heart disease is the foremost cause of death worldwide, and specialty care such as cardiologists and expensive Doppler echocardiograms are scarce in rural areas. There is significant variability among physicians in interpreting heart sounds and 2D echocardiograms from more basic, widely available tools like stethoscopes and point-of-care ultrasound devices (POCUS), leading to treatment delays. Methods 26 Deep-learning models were tested to optimally [a] stratify severity of aortic stenosis from POCUS 2D echocardiogram images [b] estimate cardiac ejection fraction (heart failure) from POCUS 2D echocardiogram videos and [c] identify pathologic heart murmurs using digital stethoscope recordings. The top performing model for each was integrated into a portable, inexpensive Raspberry-Pi device (Virtual Cardiologist), mobile application, and website. Each model was tested on two independent datasets to ensure accuracy. The heart murmur algorithm was also tested on 323 patients by recording their heart sounds via a custom and digital stethoscope in an outpatient setting for clinical validation. Results The three top-performing models outperformed cardiologists and previous AI methods. The aortic stenosis model (AUC=0.89) utilized EfficientNet, a CNN with compound scaling built on a pre-trained computational backbone. The ejection fraction model (EchoSwin) incorporated Microsoft Swin transformers modified for video input (MAE=5.6). The heart murmur model employed an LSTM Autoencoder algorithm (AUC=0.92). Conclusion The Virtual Cardiologist can serve as an accurate tool for patient assessment and triage for referral to tertiary care in remote areas with limited specialty care. This is the first tool of its kind for detection of multiple cardiovascular morbidities.