Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Vascular Regenerative Cell Deficiencies in South Asian Adults.

BACKGROUND: South Asian individuals shoulder a disproportionate burden of cardiometabolic diseases. OBJECTIVES: The purpose of this study was to determine if vascular regenerative cell content varies significantly between South Asian and White European people. METHODS: Between January 2022 and January 2023, 60 South Asian and 60 White European adults with either documented cardiovascular disease or established diabetes with ≥1 other cardiovascular risk factor were prospectively enrolled. Vascular regenerative cell content in venous blood was enumerated using a flow cytometry assay that is based on high aldehyde dehydrogenase (ALDHhi) activity and cell surface marker phenotyping. The primary outcome was the difference in frequency of circulating ALDHhi progenitor cells, monocytes, and granulocytes between the 2 groups. RESULTS: Compared with White European participants, those of South Asian ethnicity were younger (69 ± 10 years vs 66 ± 9 years; P < 0.05), had lower weight (88 ± 19 kg vs 75 ± 13 kg; P < 0.001), and exhibited a greater prevalence of type 2 diabetes (62% vs 92%). South Asian individuals had markedly lower circulating frequencies of pro-angiogenic ALDHhiSSClowCD133+ progenitor cells (P < 0.001) and ALDHhiSSCmidCD14+CD163+ monocytes with vessel-reparative capacity (P < 0.001), as well as proportionally more ALDHhi progenitor cells with high reactive oxygen species content (P < 0.05). After correction for sex, age, body mass index, and glycated hemoglobin, South Asian ethnicity was independently associated with lower ALDHhiSSClowCD133+ cell count. CONCLUSIONS: South Asian people with cardiometabolic disease had less vascular regenerative and reparative cells suggesting compromised vessel repair capabilities that may contribute to the excess vascular risk in this population. (The Role of South Asian vs European Origins on Circulating Regenerative Cell Exhaustion [ORIGINS-RCE]; NCT05253521).

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Obesity/cardiometabolic phenotype of heart failure with preserved ejection fraction: mechanisms to recent trials.

PURPOSE OF REVIEW: Heart failure with preserved ejection fraction (HFpEF) is a leading and growing cause of morbidity and mortality globally. Of the various phenotypes identified, the obesity (or cardiometabolic) phenotype appears to be most common. The purpose of this review is to provide the clinician with an abridged understanding of recent developments that have elucidated obesity/visceral adiposity as a central mechanism linking inflammation/immune dysregulation to the development of the HFpEF syndrome. Recent clinical trials examining the efficacy of pharmacological treatments that target obesity in HFpEF will also be discussed. RECENT FINDINGS: Recent data indicate that visceral adiposity and insulin resistance in HFpEF serve as key mechanisms driving inflammation and immune dysregulation, which play a critical role in the development of cardiac stiffness, diastolic dysfunction and fibrosis in HFpEF. In obesity, alterations in macrophage polarization, changes in innate and adaptive immune systems and altered myocardial energetics promote metabolic inflammation in HFpEF. Finally, emerging data suggest that inflammatory biomarkers, specifically, IL-6, may provide useful information about HFpEF severity and symptom burden in obesity. SUMMARY: The obesity phenotype of HFpEF is seen in upward of 80% with HFpEF. Obesity is not just a bystander, but plays an essential role in the pathobiology and clinical course of HFpEF. Targeting overweight/obesity in HFpEF with GLP-1 receptor agonists holds promise in these patients.

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

OBJECTIVE: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). BACKGROUND: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools. METHODS: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair. RESULTS: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR.

Temporal trends in mortality of aortic dissection and rupture in the UK, Japan, the USA and Canada.

OBJECTIVE: Aortic dissection and aortic aneurysm rupture are aortic emergencies and their clinical outcomes have improved over the past two decades; however, whether this has translated into lower mortality across countries remains an open question. The purpose of this study was to compare mortality trends from aortic dissection and rupture between the UK, Japan, the USA and Canada. METHODS: We analysed the WHO mortality database to determine trends in mortality from aortic dissection and rupture in four countries from 2000 to 2019. Age-standardised mortality rates per 100 000 persons were calculated, and annual percentage change was estimated using joinpoint regression. RESULTS: Age-standardised mortality rates per 100 000 persons from aortic dissection and rupture in 2019 were 1.04 and 1.80 in the UK, 2.66 and 1.16 in Japan, 0.76 and 0.52 in the USA, and 0.67 and 0.81 in Canada, respectively. There was significantly decreasing trends in age-standardised mortality from aortic rupture in all four countries and decreasing trends in age-standardised mortality from aortic dissection in the UK over the study period. There was significantly increasing trends in mortality from aortic dissection in Japan over the study period. Joinpoint regression identified significant changes in the aortic dissection trends from decreasing to increasing in the USA from 2010 and Canada from 2012. In sensitivity analyses stratified by sex, similar trends were observed. CONCLUSIONS: Trends in mortality from aortic rupture are decreasing; however, mortality from aortic dissection is increasing in Japan, the USA and Canada. Further study to explain these trends is warranted.

The Global Transcription Regulator XooClp Governs Type IV Pili System-Mediated Bacterial Virulence by Directly Binding to TFP-Chp Promoters to Coordinate Virulence Associated Functions.

Type IV pili (TFP) play a crucial role in the sensing of the external environment for several bacteria. This surface sensing is essential for the lifestyle transitions of several bacteria and involvement in pathogenesis. However, the precise mechanisms underlying TFP's integration of environmental cues, particularly in regulating the TFP-Chp system and its effects on Xanthomonas physiology, social behavior, and virulence, remain poorly understood. In this study, we focused on investigating Clp, a global transcriptional regulator similar to CRP-like proteins, in Xanthomonas oryzae pv. oryzae, a plant pathogen. Our findings reveal that Clp integrates environmental cues detected through diffusible signaling factor (DSF) quorum sensing into the TFP-Chp regulatory system. It accomplishes this by directly binding to TFP-Chp promoters in conjunction with intracellular levels of cyclic-di-GMP, a ubiquitous bacterial second messenger, thereby controlling TFP expression. Moreover, Clp-mediated regulation is involved in regulating several cellular processes, including the production of virulence-associated functions. Collectively, these processes contribute to host colonization and disease initiation. Our study elucidates the intricate regulatory network encompassing Clp, environmental cues, and the TFP-Chp system, providing insights into the molecular mechanisms that drive bacterial virulence in Xanthomonas spp. These findings offer valuable knowledge regarding Xanthomonas pathogenicity and present new avenues for innovative strategies aimed at combating plant diseases caused by these bacteria. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

2023 Update on equity, diversity, and inclusion in Canadian cardiac surgery.

PURPOSE OF REVIEW: Despite efforts to diversify the medical field, cardiac surgery remains amongst the least diverse specialties. Specifically, the percentage of women and racial minorities has remained low in past few decades. This may impact prospective trainee recruitment and surgical care. This paper highlights recent efforts that aim to promote diversity and inclusion of the Canadian cardiac surgical workforce. RECENT FINDINGS: Formal programs have been established to support students at different stages of training. In 2022, the Canadian Society for Cardiac Surgery has released an equity, diversity, and inclusion statement to summarize the current state and the strategic goals to accomplish a more just working environment. At the local level, the University of Toronto Next Surgeon high school pilot program, provided low-income, women, and racial minority students mentorship and experiential exposure to our field. Also, the University of Toronto, scholarships funded summer research with cardiac surgeons for women, as well as Black and Indigenous medical students. SUMMARY: Tangible efforts that target high school, undergraduate, and medical students are underway to promote equity and diversity of cardiac surgeons in Canada. Future studies that evaluate the gaps and identify bottlenecks could better guide interventions at institutions across the country.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.

Wellness and burnout in cardiac surgery: not black and white.

PURPOSE OF REVIEW: Despite a growing emphasis on burnout in medicine, there remains a paucity of data in cardiac surgery. Herein, we summarize recent data on cardiac surgeon well being and identify factors for consideration in future burnout inquiries and management. RECENT FINDINGS: Overall, 70-90% of cardiothoracic surgeons report job satisfaction in the United States. However, 35-60% still endorse burnout symptoms, and the specialty reports some of the highest rates of depression (35-40%) and suicidal ideation (7%). Such negative experiences are greater among early-stage and female surgeons and may be addressed through targeted, program-specific wellness policies. Canada's single-payer healthcare system might exacerbate surgeon burnout due to lower financial compensation and job autonomy. SUMMARY: Cardiothoracic surgeons appear simultaneously burnt out and professionally fulfilled. They report a high incidence of depression and clock in the most hours, yet the majority would choose this specialty again. These findings reveal a more nuanced state of well being than previously appreciated and speak to ambiguities in how burnout is conceived and measured. A broader examination across surgical and social contexts highlights the hierarchical nature of burnout factors and potential ways forward. Collectively, these insights can inform assessments of burnout in Canadian cardiac surgery that remain absent to date.

Effect of empagliflozin on cardiac remodelling in South Asian and non-South Asian individuals: insights from the EMPA-HEART CardioLink-6 randomised clinical trial.

BACKGROUND: This exploratory sub-analysis of the EMPA-HEART CardioLink-6 trial examined whether the previously reported benefit of the sodium-glucose cotransporter 2 (SGLT2) inhibitor empagliflozin on left ventricular (LV) mass (LVM) regression differs between individuals of South Asian and non-South Asian ethnicity. METHODS: EMPA-HEART CardioLink-6 was a double-blind, placebo-controlled clinical trial that randomised 97 individuals with type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) to either empagliflozin 10 mg daily or placebo for 6 months. LV parameters and function were assessed using cardiac magnetic resonance imaging. The 6-month changes in LVM and LV volumes, all indexed to baseline body surface area, for South Asian participants were compared to those for non-South Asian individuals. RESULTS: Compared to the non-South Asian group, the South Asian sub-cohort comprised more males, was younger and had a lower median body mass index. The adjusted difference for LVMi change over 6 months was -4.3 g/m2 (95% confidence interval [CI], -7.5, -1.0; P = 0.042) for the South Asian group and -2.3 g/m2 (95% CI, -6.4, 1.9; P = 0.28) for the non-South Asian group (Pinteraction = 0.45). There was no between-group difference for the adjusted differences in baseline body surface area-indexed LV volumes and LV ejection fraction. CONCLUSIONS: There was no meaningful difference in empagliflozin-associated LVM regression between South Asian and non-South Asian individuals living with T2DM and CAD in the EMPA-HEART CardioLink-6 trial. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02998970 (First posted on 21/12/ 2016).

Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.

Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021. Input features included 36 preoperative demographic/clinical variables. The primary outcome was 30-day major adverse cardiovascular events (composite of stroke, myocardial infarction, or death). The data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary metric for evaluating model performance was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Overall, 38 853 patients underwent CEA during the study period. Thirty-day major adverse cardiovascular events occurred in 1683 (4.3%) patients. The best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.90-0.92). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.62 (95% CI, 0.60-0.64), and existing tools in the literature demonstrate area under the receiver operating characteristic curve values ranging from 0.58 to 0.74. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.02. The strongest predictive feature in our algorithm was carotid symptom status. Conclusions The machine learning models accurately predicted 30-day outcomes following CEA using preoperative data and performed better than existing tools. They have potential for important utility in guiding risk-mitigation strategies to improve outcomes for patients being considered for CEA.

First Report of Cucurbit aphid-borne yellows virus infecting five cucurbits in India.

Cucurbits are among the most popular vegetables cultivated globally. They have high economic importance, especially in India, where they are cooked and eaten as vegetables (Dhillon et al. 2016). In February 2023, yellowing symptoms were observed on cucurbitaceous species, viz. Trichosanthes cucumerina (Snake gourd - SG), Luffa acutangula (Ridge gourd - RG), Lagenaria siceraria (Bottle gourd - BG), Luffa aegyptiaca (Sponge gourd - SPG) and yellow chlorotic spots were recorded on Benincasa hispida (Ash gourd - AG) growing in the experimental farm at the Indian Agricultural Research Institute, Regional Station, Pune (Supplementary Figure 1). The average disease incidence ranged from 5% to 30%. A total of 175 leaf samples, including thirty symptomatic and five asymptomatic plants of each cucurbit, were collected and tested by DAS-ELISA using antisera against cucurbit aphid-borne yellows virus (CABYV) (DSMZ, Germany), cucurbit yellow stunting disorder virus (CYSDV) (Arsh Biotech, India), cucumber mosaic virus (CMV), zucchini yellow mosaic virus (ZYMV), and papaya ringspot virus (PRSV) (Agdia, USA). All 150 symptomatic cucurbit samples tested positive for CABYV, while five samples from SG, 14 from RG, two from AG, and 11 from SPG hosts were also positive for PRSV. Asymptomatic samples were negative for all viruses tested. In order to further confirm the presence of the virus, total RNA was extracted from ten samples of each cucurbit host that were positive only for CABYV and the asymptomatic samples using the RNeasy Plant Mini Kit (Qiagen, Germany) as per the manufacturer's protocol. Two-step RT-PCR was carried out using the extracted RNA and CABYV-specific primers, amplifying c. 484 bp of the coat protein gene region (Boubourakas et al. 2006). Amplicons of expected size were obtained in all symptomatic samples, whereas the asymptomatic samples tested negative. Three amplicons obtained from positive samples from each of the cucurbit species were directly sequenced and found to be identical to each other. A representative virus sequence obtained from each cucurbit was deposited in GenBank (Snake gourd - OQ921128, Ridge gourd - OQ921127, Bottle gourd - OQ921126, Ash gourd - OQ921125, Sponge gourd - OQ921129). In BLASTn analysis, the isolates shared from 94.23 to 100% nucleotide identities with the Indian CABYV isolates of various cucurbits and clustered closely with other Pune isolates in the phylogenetic analysis (Supplementary Figure 2). CABYV (genus Polerovirus) is a single-stranded positive-sense RNA virus, and is known to infect and cause severe economic losses in cucurbits worldwide. Previously, occurrences of CABYV have been reported in cucurbits such as watermelon, bitter gourd, cucumber, squash, teasel gourd, and muskmelon in India (Nagendran et al. 2022; Tripathi et al. 2023). It has also been reported to infect a weed species - Abelmoschus moschatus from the same geographical region (Verma et al. 2023). To our knowledge, this is the first report of the natural occurrence of CABYV in snake gourd and ridge gourd worldwide and bottle gourd, ash gourd and sponge gourd in India. The present findings have significant epidemiological importance, as they demonstrate that CABYV is spreading to other cucurbits and occurring widely in India.

Empagliflozin improves circulating vascular regenerative cell content in people without diabetes with risk factors for adverse cardiac remodeling.

Sodium glucose-cotransporter 2 (SGLT2) inhibitors have been reported to reduce cardiovascular events and heart failure in people with and without diabetes. These medications have been shown to counter regenerative cell exhaustion in the context of prevalent diabetes. This study sought to determine if empagliflozin attenuates regenerative cell exhaustion in people without diabetes. Peripheral blood mononuclear cells were collected at the baseline and 6-mo visits from individuals randomized to receive empagliflozin (10 mg/day) or placebo who were participating in the EMPA-HEART 2 CardioLink-7 trial. Precursor cell phenotypes were characterized by flow cytometry for cell-surface markers combined with high aldehyde dehydrogenase activity to identify precursor cell subsets with progenitor (ALDHhi) versus mature effector (ALDHlow) cell attributes. Samples from individuals assigned to empagliflozin (n = 25) and placebo (n = 21) were analyzed. At baseline, overall frequencies of primitive progenitor cells (ALDHhiSSClow), monocyte (ALDHhiSSCmid), and granulocyte (ALDHhiSSChi) precursor cells in both groups were similar. At 6 mo, participants randomized to empagliflozin demonstrated increased ALDHhiSSClowCD133+CD34+ proangiogenic cells (P = 0.048), elevated ALDHhiSSCmidCD163+ regenerative monocyte precursors (P = 0.012), and decreased ALDHhiSSCmidCD86 + CD163- proinflammatory monocyte (P = 0.011) polarization compared with placebo. Empagliflozin promoted the recovery of multiple circulating provascular cell subsets in people without diabetes suggesting that the cardiovascular benefits of SGLT2 inhibitors may be attributed in part to the attenuation of vascular regenerative cell exhaustion that is independent of diabetes status.NEW & NOTEWORTHY Using an aldehyde dehydrogenase (ALDH) activity-based flow cytometry assay, we found that empagliflozin treatment for 6 mo was associated with parallel increases in circulating vascular regenerative ALDHhi-CD34/CD133-coexpressing progenitors and decreased proinflammatory ALDHhi-CD14/CD86-coexpressing monocyte precursors in individuals without diabetes but with cardiovascular risk factors. The rejuvenation of the vascular regenerative cell reservoir may represent a mechanism via which sodium glucose-cotransporter 2 (SGLT2) inhibitors limit maladaptive repair and delay the development and progression of cardiovascular diseases.

Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair.

BACKGROUND: Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. RESULTS: Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95-0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68-0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis. CONCLUSIONS: In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.

Cost of treating sick young infants (0-59 days) with Possible Serious Bacterial Infection in resource-constrained outpatient primary care facilities: An insight from implementation research in two districts of Haryana and Uttar Pradesh (India).

Background: Information on the average and incremental costs of implementing alternative strategies for treating young infants 0-59 days old in primary health facilities with signs of possible serious bacterial infection (PSBI) when a referral is not feasible is limited but valuable for policymakers. Methods: Direct activity costs were calculated for outpatient treatment of PSBI and pneumonia in two districts of India: Palwal, Haryana and Lucknow, Uttar Pradesh. These included costs of staff time and consumables for initial assessment, classification, and referrals; recommended treatment of fast breathing (oral amoxicillin for seven days) and PSBI (injection gentamicin and oral amoxicillin for seven days); and daily assessments. Indirect operational costs included staff training; staff time cost for general management, supervision, and coordination; referral transport; and communication. Results: The average cost per young infant treated for recommended and acceptable treatment for PSBI was 16 US dollars (US$) (95% CI = US$15.4-16.3) in 2018-19 and US$18.5 in 2022 (adjusted for inflation) when all direct and indirect operational costs were considered. The average cost of recommended treatment for pneumonia was US$10.1 (95% CI = US$9.7-10.6) or US$11.7 in 2022, per treated young infant. The incremental cost 2018-2019 for supplies, medicines, and operations (excluding staff time costs) per infant treated for PSBI was US$6.1 and US$4.3 and for pneumonia was US$3.5 and US$2.2 in Palwal and Lucknow, respectively. Operation and administrative costs were 25% in Palwal and 12% in Lucknow of the total PSBI treatment costs. The average cost per live birth for treating PSBI in each population was US$5 in Palwal and US$3 in Lucknow. Higher operation costs for social mobilisation activities in Palwal led to the empowerment of families and timely care-seeking. Conclusions: Costs of treatment of PSBI with the recommended regimen in an outpatient setting, when a referral is not feasible, are under US$20 per treated child and must be budgeted to reduce deaths from neonatal sepsis. The investment must be made in activities that lead to successful identification, prompt care seeking, timely initiation of treatment and follow-up.

Using machine learning to predict outcomes following open abdominal aortic aneurysm repair.

OBJECTIVE: Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair. METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures. RESULTS: Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.

Sword Bean (Canavalia gladiata): a new host of Bean common mosaic virus.

BACKGROUND: Sword bean (Canavalia gladiata) is an underutilized legume that has the potential to become an important food source owing to its wide range of nutritional and medicinal properties. In May 2023, symptoms induced by a possible virus infection such as mosaic, mottling and vein banding were observed on the leaves of about 20% of the Sword bean plants growing at the experimental research farm of the Indian Agricultural Research Institute in Pune, Maharashtra, India. METHODS AND RESULTS: Symptomatic and asymptomatic samples were screened by ELISA for the presence of Potyvirus, Cucumber mosaic virus and Tobacco mosaic virus. All symptomatic samples tested positive for Potyvirus in ELISA as well as in RT-PCR assay using the universal potyvirus primer pair (CPUP /P9502) which amplify c. 700 bp of the partial coat protein region and 3'UTR. Asymptomatic samples tested negative for all tested viruses in both serological and molecular assays. BLASTn sequence analysis of the amplicons revealed that the sequence shares more than 98% identity with an Indian isolate of Bean common mosaic virus (BCMV). Sequence analysis enabled the identification of the Potyvirus as BCMV. Furthermore, the present Sword bean isolate clustered with other BCMV isolates in the phylogenetic analysis. CONCLUSION: In the present study, BCMV was found to be naturally infecting Sword bean for the first time in the world. This is of epidemiological importance, as BCMV is known to cause significant yield losses in legumes and could severely hamper Sword bean production.