Citations

ABSTRACTBackgroundDespite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.MethodsUsing Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.ResultsThe median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re‐transplant patients characterized Cluster 1. Cluster 2 was characterized by first‐time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3‐year patient death, and graft failure. Cluster 3 had a higher risk of death‐censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient‐, graft‐, and death‐censored graft survival at 1 and 3 years. Compared to non‐AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non‐AKI: 25.5%); however, there were comparable long‐term outcomes specific to death‐censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non‐AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non‐AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non‐AKI: 86.0%).ConclusionsIn this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

AbstractObjectiveTransgender and gender‐diverse (TGD) individuals have a gender identity or expression that differs from the sex assigned to them at birth. They are an underserved population who experience health care inequities. Our primary objective was to identify if there are treatment differences between TGD and cisgender lesbian/gay/bisexual/queer (LGBQ) or heterosexual individuals presenting with abdominal pain to the emergency department (ED).MethodsRetrospective observational cohort study of patients ≥12 years of age presenting to 21 EDs within a health care system with a chief complaint of abdominal pain between 2018 and 2022. TGD patients were matched 1:1:1:1 to cisgender LGBQ women and men and cisgender heterosexual women and men, respectively. Propensity score matching covariates included age, ED site, mental health history, and gastrointestinal history. The primary outcome was pain assessment within 60 min of arrival. The secondary outcome was analgesics administered in the ED.ResultsWe identified 300 TGD patients, of whom 300 TGD patients were successfully matched for a total cohort of 1300 patients. The median (IQR) age was 25 (20–32) years and most patients were treated in a community ED (58.2%). There was no difference between groups in pain assessment within 60 min of arrival (59.0% TGD vs. 63.2% non TGD, p = 0.19). There were no differences in the number of times pain was assessed (median [IQR] 2 [1–3] vs. 2 [1–4], p = 0.31) or the severity of pain between groups (5.5 [4–7] vs. 6 [4–7], p = 0.11). TGD patients were more likely to receive nonsteroidal anti‐inflammatory drugs (32.0% vs. 24.9%, p = 0.015) and less likely to receive opioids than non‐TGD patients (24.7% vs. 36.9%, p = <0.001). TGD and nonbinary patients, along with LGBQ cisgender women (24.7%) and heterosexual cisgender women (34%), were less likely to receive opioids than LGBQ cisgender men (54%) and heterosexual cisgender men (42.3%, p < 0.01).ConclusionThere was no difference in frequency of pain assessment, regardless of gender identity or sexual orientation. More cisgender men, compared to TGD and cisgender women, received opioids for their pain.

AbstractObjectivesInterpatient variability in bipolar I depression (BP‐D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).MethodsSupervised machine learning models were trained on data from BP‐D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission.ResultsAn AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine‐treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week‐4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week‐4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45–5.76; p < 9.3E‐11).ConclusionMachine learning strategies achieved cross‐trial and cross‐drug replication in predicting remission after 8 weeks of pharmacotherapy for BP‐D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.

Abstract Genome-wide association studies (GWAS) have identified over 300 loci associated with the inflammatory bowel diseases (IBD), but putative causal genes for most are unknown. We conducted the largest disease-focused expression quantitative trait loci (eQTL) analysis using colon tissue from 252 IBD patients to determine genetic effects on gene expression and potential contribution to IBD. Combined with two non-IBD colon eQTL studies, we identified 194 potential target genes for 108 GWAS loci. eQTL in IBD tissue were enriched for IBD GWAS loci colocalizations, provided novel evidence for IBD-associated genes such as ABO and TNFRSF14, and identified additional target genes compared to non-IBD tissue eQTL. IBD-associated eQTL unique to diseased tissue had distinct regulatory and functional characteristics with increased effect sizes. Together, these highlight the importance of eQTL studies in diseased tissue for understanding functional consequences of genetic variants, and elucidating molecular mechanisms and regulation of key genes involved in IBD.

Abstract Accumulating evidence suggests dysregulated immune checkpoint (IC) signaling can exacerbate COVID-19 severity, but the role of these molecules in the pathogenesis of fatal COVID-19-related diffuse alveolar damage (DAD) remains elusive. Understanding how IC proteins influence acute lung injury due to COVID-19 can provide insights into potential therapeutic strategies to modulate immune responses and improve patient outcomes. Here, in a single-center autopsy cohort, we determined the cellular localization of ICs in lung tissue from cases of fatal COVID-19, DAD-comparators, and non-fibrotic controls by using immunohistochemistry, and investigated their association with clinical outcomes. We expanded our findings by performing analyses of publicly available single-cell RNA sequencing datasets from patients with fatal COVID-19 and non-fibrotic controls. We demonstrated the presence of protein-protein interaction networks of ICs in the lung cellular niche by performing transcriptomic profiling of lung tissue-derived RNA counts from patients with fatal COVID-19. Further, we leveraged data from the largest international, multi-center COVID-19 autopsy cohort and validated that, among patients with fatal COVID-19, those with higher PD-L1/CD274 expression in lung endothelial cells had more rapid clinical deterioration. Lastly, in a cohort of individuals with early COVID-19, IC plasma protein levels were elevated in those with persistent SARS-CoV-2 RNAemia and adverse clinical outcomes. Collectively, our data provide unique pathological insights into the role of IC dysregulation and differential disease severity in COVID-19.

ABSTRACTThis single‐center retrospective study investigated subclinical rejection prevalence and significance in simultaneous pancreas and kidney transplant (SPKT) recipients. We analyzed 352 SPKT recipients from July 2003 to April 2022. Our protocol included pancreas allograft surveillance biopsies at 1, 4, and 12months post‐transplant. After excluding 153 patients unable to undergo pancreas biopsy, our study cohort comprised 199 recipients. Among the 199 patients with protocol pancreas biopsies, 107 had multiple protocol pancreas biopsies in the first year, totaling 323. Subclinical rejection was identified in 132 episodes (41%). Of these, 72% were Grade 1, 20% were indeterminate, and 8% were Banff Grade 2 or higher. All episodes of subclinical rejection were treated. Rates of pancreas graft loss (10% vs. 7%) and clinical rejection (21% vs. 20%) at 3 years were similar between those with and without subclinical rejection. Subclinical rejection Banff Grade 2 or more was associated with poor pancreas graft survival HR of 5.5 (95% CI: 1.24–24.37, p = 0.025). Of 236 simultaneous protocol kidney and pancreas biopsies, 102 (43%) showed pancreas subclinical rejection, while only 17% had concurrent kidney subclinical rejection. Our findings suggest limited predictive value of pancreatic enzymes and euglycemia in detecting pancreas rejection. Furthermore, poor concordance existed between pancreas and kidney subclinical rejection.

Abstract Background Perianal draining tunnels in hidradenitis suppurativa (HS) and perianal fistulizing inflammatory bowel disease (IBD) present diagnostic and management dilemmas. Methods We conducted a retrospective chart review of patients with perianal disease evaluated at Mayo Clinic from January 1, 1998, through July 31, 2021. Patients' demographic and clinical data were extracted, and 28 clinical features were collected. After experimenting with several machine learning techniques, random forests were used to select the 15 most important clinical features to construct the diagnostic prediction model to distinguish perianal HS from fistulizing perianal IBD. Results A total of 263 patients were included (98 with HS, 100 with IBD, and 65 with both IBD and HS). Patients with HS had a higher mean body mass index, a higher smoking rate, and more commonly showed cutaneous manifestations of tunnels and comedones, while fistulas, abscesses, induration, anal tags, ulcers, and anal fissures were more common in patients with IBD. In addition to having lesions in the perianal area, patients with IBD often had lesions in the buttocks and perineum, while those with HS had additional lesions in the axillae and groin. Among the statistically significant features, the 15 most important were identified by random forest: fistula , tunnel , digestive symptom , knife‐cut ulcer , perineum , body mass index , age , axilla , abscess , tags , smoking , groin , genital cutaneous edema , erythema , and bilateral/unilateral . Conclusions The results of this study may help differentiate perianal lesions, especially perineal HS and fistulizing perineal IBD, and provide promise for a better therapeutic outcome.