Search

Limited data exist on the effect of preoperative statin therapy on postoperative respiratory complications. Machine learning algorithms (MLA) can process large, heterogenous data, and have immensely improved the ability for risk prediction. In this study, we sought to examine the role of preoperative statins on respiratory complications in patients undergoing coronary artery bypass grafting (CABG) using MLA.The study population contained the data of patients who underwent CABG between the years 2015 and 2019 (n = 5638). Three hundred and thirty-seven independent variables were recorded and the data was randomly split with stratified sampling into training and testing data with 20% of the data (1113 records) reserved for model testing. Various models including linear models, Random forest, SVM, and XGboost were trained to predict the incidence of postoperative respiratory complications. Forty-seven important features were found to impact model prediction (p ≤ .05) using the global surrogate model method. A conventional multivariable linear regression model was then used to identify predictors of respiratory complications.One thousand three hundred sixty-two (24.5%) patients developed a respiratory complication in our series. The respiratory complication was seen in 561 (29.7%) of the patients who were not on statin compared to only 801 (21.8%) who were on a statin, p .0001. The area under the curve for receiver operating characteristic curve using statins and respiratory complications was 0.706. Statins showed positive feature importance in all the MLA models.MLA showed that statins impacted the prediction of respiratory complications in all the models studied. The study confirmed that preoperative statins reduced the risk of respiratory complications by 21%.

A consistent decline in the number of applicants to cardiothoracic and vascular surgery (CTVS) training programs has been observed across the country over the last few years. This disenchantment with the speciality will lead to a shortage of trained CTVS surgeons at a time when incidence of cardiovascular diseases is continuously rising in India.The aim of the study was to reflect on the reasons that averted applicants from pursuing a career in CTVS and measures that would make CTVS a more attractive option to future applicants.An online nationwide prospective cross-sectional survey was conducted. The participants were from a pool of general surgery residents/residents who had just completed the training. A 5-point Likert scale was utilized to rate and evaluate the factors influencing CTVS as the primary choice, factors influencing any super-speciality, and factors that would aid in decision-making and encourage an interest in CTVS.A total of 618 responses were obtained, 25.6% (Although surgical residents report early interest in CTVS, a lack of adequate exposure to the speciality leads to a decline in interest in CTVS. An overhaul of the surgical training that mandates rotating to CTVS, increased interactions with medical students and surgical trainees by CTVS faculty, and incorporation of catheter-based training in the CTVS syllabus is urgently required to increase the number of applicants to CTVS.The online version contains supplementary material available at 10.1007/s12055-022-01406-2.

Early repair is recommended in type A aortic dissection repair, however, this is not often possible. The aim of this study was to assess the time delays, examine the effect of timing and malperfusion on outcomes, and identify other independent risk factors for mortality.This was a retrospective study on data collected prospectively. Primary outcome was 30-day mortality. Secondary outcome was adverse postoperative outcomes. All patients who arrived at the hospital with a type A aortic dissection, irrespective of the timing, were included in the study. Univariate as well as multivariate analysis was carried out.A total of 81 patients presented to our institution during the study period. Half the patients got operated within 86 h of symptom onset and 75% were operated within 183 h. With regards to malperfusion, 6 (8.8%) patients had cerebral malperfusion, 10 (14.7%) had features of myocardial ischemia, and 13 (19.1%) had renal malperfusion. One (1.5%) patient had mesenteric ischemia and 9 (13.2%) had limb ischemia. The duration between symptom onset and operative repair was longer in survivors (152 ± 167 vs. 75 ± 102, p = .29). A total of 29 (42.6%) patients presented with features of one or more organ malperfusion and the mortality in these patients was significantly higher at 10 (34.5%) versus 5 (12.5%); p = .04. Multivariable logistic regression identified CPB time as an independent risk factor in our cohort for 30-day mortality (odds ratio: 1.03; 95% confidence interval: 1.007-1.06).In cases of predominantly delayed presentation and operative repair, the outcome is significantly influenced by presence of malperfusion. Surgical repair still produces satisfactory outcomes and should be undertaken at the earliest in most cases.

Inadequate excision, seeding, multicentricity and malignancy are often ascribed for recurrence of atrial myxomas. Differentiation between benign myxomas and malignant myxofibrosarcomas can be challenging, and a high index of clinical suspicion and vimentin staining on immunohistochemistry is needed to confirm the diagnosis. Early recurrence is common in these patients and completeness of excision is the key. Prognosis despite successful excision is poor. We report a benign atrial myxoma in a young female patient which upon recurrence turned out to be a myxofibrosarcoma.

Late presentation of aortopulmonary window (APW) beyond infancy is uncommon and many of these cases are inoperable due to development of progressive pulmonary hypertension and Eisenmenger syndrome. Outcome data in this cohort is thus sparse and the aim of this study was to analyze the outcomes in patients with APW operable beyond 1 year of age. Between September 2016 and March 2020, in a single center, 12 consecutive patients older than 1 year, undergoing surgery for APW, were included in the study. The median age and weight at presentation were 7.5 years (interquartile range (IQR) 4–9.5) and 15 kg (IQR 11.7–19.5). Ten (83.3%) patients had type 1 APW (proximal type) and 2 (16.6%) had a type 2 APW (distal type). Eight (66.6%) patients had associated lesions. Transaortic patch closure of APW was done in all cases. Seven (58.3%) patients were extubated within 3.5 h of admission in intensive care. There were no early deaths or during follow-up. The median follow-up duration was 20.5 months (IQR 7.5–24), and all patients were in New York Heart Association (NYHA) class I at last follow-up. Follow-up echocardiography did not reveal any significant residual shunts necessitating any additional procedure and a consistent decrease in pulmonary artery pressures. Surgery in patients with APW beyond 1 year of age is possible in selected patients. The early and intermediate surgical outcomes in patients who remain operable are excellent.

ObjectivesThe Society of Thoracic Surgeons (STS), and EuroSCORE II (ES II) risk scores, are the most commonly used risk prediction models for adult cardiac surgery post-operative in-hospital mortality. However, they are prone to miscalibration over time, and poor generalisation across datasets and their use remain controversial. It has been suggested that using Machine Learning (ML) techniques, a branch of Artificial intelligence (AI), may improve the accuracy of risk prediction. Despite increased interest, a gap in understanding the effect of dataset drift on the performance of ML over time remains a barrier to its wider use in clinical practice. Dataset drift occurs when a machine learning system underperforms because of a mismatch between the dataset it was developed and the data on which it is deployed. Here we analyse this potential concern in a large United Kingdom (UK) database.MethodsA retrospective analyses of prospectively routinely gathered data on adult patients undergoing cardiac surgery in the UK between 2012-2019. We temporally split the data 70:30 into a training and validation subset. ES II and five ML mortality prediction models were assessed for relationships between and within variable importance drift, performance drift and actual dataset drift using temporal and non-temporal invariant consensus scoring, combining geometric average results of all metrics as the Clinical Effective Metric (CEM).ResultsA total of 227,087 adults underwent cardiac surgery during the study period with a mortality rate of 2.76%. There was a strong evidence of decrease in overall performance across all models (p < 0.0001). Xgboost (CEM 0.728 95CI: 0.728-0.729) and Random Forest (CEM 0.727 95CI 0.727-0.728) were the best overall performing models both temporally and non-temporally. ES II perfomed worst across all comparisons. Sharp changes in variable importance and dataset drift between 2017-10 to 2017-12, 2018-06 to 2018-07 and 2018-12 to 2019-02 mirrored effects of performance decrease across models.ConclusionsCombining the metrics covering all four aspects of discrimination, calibration, clinical usefulness and overall accuracy into a single consensus metric improved the efficiency of cognitive decision-making. All models show a decrease in at least 3 of the 5 individual metrics. CEM and variable importance drift detection demonstrate the limitation of logistic regression methods used for cardiac surgery risk prediction and the effects of dataset drift. Future work will be required to determine the interplay between ML and whether ensemble models could take advantage of their respective performance advantages.Central messageML performance decreases over time due to dataset drift, but remains superior to ES II. Therefore regular assessment and modification of ML models may be preferable.Prospective messageA gap in understanding the effect of dataset drift on the performance of ML models over time presents a major barrier to their clinical application. Xgboost and Random Forest have shown superior performance both temporally and non-temporally against ES II. However, a decrease in model performance of all models due to dataset drift suggests the need for regular drift monitoring.

An elderly patient with significant aortic regurgitation presented with heart failure. Dilation of the aortic root precluded a transcatheter anatomic site valve implantation, and prohibitive operative risk ruled against surgical implantation. A bail-out transcatheter implantation of the aortic valve in the descending aorta was successfully carried out with satisfactory outcomes.

Significant dilemma exists regarding the management of the aortic root pathology in acute aortic dissections. Several strategies for both repair and replacement exist and there is a lack of clarity on the superiority of one over the other. Important factors that influence management strategies include involvement of the sinuses, the competence of the aortic valve, the presence of Marfans syndrome, and connective tissue disorders, as well as availability of surgical expertise. The wide variability in these factors makes it unlikely for any one technique to be suitable for the management of all aortic roots pathology.

Management of asymptomatic patients with severe aortic stenosis remains debatable. Until now, evidence on the subject was limited to observational studies, registry data, and meta-analyses. Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis trial thus is the first randomized controlled trial assessing the role of surgery in asymptomatic patients with severe aortic stenosis. A critical evaluation of the study along with discussion on existing evidence has been provided in this appraisal.

The long-term outcome of the Randomized Comparison of Coronary Artery Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease trial has recently been published. This appraisal provides a critical evaluation of the main findings of the study.

Aim Concerns have been raised over patient outcomes following implantation of small aortic valves (size: 19 and 17 mm). However, in patients with a smaller body surface area, these valves may be adequate. The aim of th study was to assess the hemodynamic and functional performance of these valves and their impact on clinical outcomes in patients with a small aortic root. Material and methods This was a prospective observational study that included all consecutive patients undergoing aortic valve replacement (AVR) with a small-sized aortic valve over a 3-year period. Patients were followed up at 1 week, 6 weeks, and 1 year. Functional and clinical evaluation along with echocardiography was carried out for hemodynamic assessment. In-hospital mortality and hemodynamic outcomes at 1-year follow-up were recorded. Results Isolated AVR with a size 17 mm valve was carried out in 15 (25%) and with a 19 mm valve in 45 (75%) patients. The mean annular size was 19.12 ± 2.03 mm. The mean indexed effective orifice area was 1.08 ± 0.16 cm2/m2. Satisfactory decrease in peak and mean trans-prosthetic gradient were evident (peak gradient preoperatively was 92.15 ± 26.2 mmHg, and 25.68 ± 12.28 mmHg at 1 year, mean gradient was 55.31 ± 17.41 mmHg preoperatively and 13.71 ± 7.39 mmHg at 1 year). The functional status of patients also showed significant improvement post AVR. Left ventricular ejection fraction pre-operatively was 59.67% ± 10.38% and 59.57% ± 7.98% at 1-week, 59.15% ± 8.17% at 6 weeks, and 59.59% ± 7.48% at 1 year. Conclusion When confronted with a small aortic root, AVR with a small-sized prosthesis provides a satisfactory hemodynamic and functional outcome. In patients with small body surface area, implantation of a small-sized valve is a viable option.

There is a lack of clarity on whether to choose a mechanical or a bioprosthetic valve for aortic valve replacement in patients between the age of 50 and 70.In this review, the existing evidence on the subject has been evaluated.Decision-making in this age group is heavily dependent on patient choice and patient-related factors. Mechanical valves in this age group are associated with an increased risk of bleeding while reoperations remain an important concern with bioprosthetic valves. Stroke rates have been reported to be largely similar in both valve types.There appears to be conflicting data on survival with some studies suggesting a survival benefit with mechanical valves.

Risk stratification plays a major role in the clinical decision-making process, patient consent and clinical governance analysis. However, the calibration of current risk scores (e.g., European System for Cardiac Operative Risk Evaluation (EuroSCORE), The Society of Thoracic Surgeons (STS) risk score) has been shown to deteriorate over time – a process known as calibration drift. The introduction of new clinical scores with different variable sets typically result in disparate datasets due to different levels of missingness. This is a barrier to the full insight and predictive capability of datasets across all potentially available time ranges. Little is known about the use of ensemble learning with ensemble metrics to mitigate the effects of calibration drift and changing risk across siloed datasets and time. In this study, we evaluated the effect of various combinations of Machine Learning (ML) models in improving model performance. The National Adult Cardiac Surgery Audit dataset was used (January 1996 to March 2019, 647,726 patients). We trained six different base learner models including Logistic Regression, Neuronetwork, Random Forest (RF), Weighted Support Vector Machine, Xgboost and Bayesian Update, based on two different variable sets of either Logistic EuroScore (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996–2011 and 2012–2019). These base learner models are ensembled using nine different combinations to produce homogeneous or heterogeneous ensembles. Discrimination, calibration, clinical effectiveness and overall accuracy were assessed using an ensemble metric, referred to as clinical effectiveness metric (CEM). Xgboost homogenous ensemble (HE) was the highest performing model (CEM 0.725) with AUC (0.8327; 95% Confidence Interval (CI) 0.8323–0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320–0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996–2011 (t-test adjusted, p = 1.67e-6) or 2012–2019 (t-test adjusted, p = 1.35e-193) datasets alone. Both homogenous and heterogenous ML ensembles performed significantly better than traditional recalibration method (Bayesian Update). Combining the metrics covering all four aspects of discrimination, calibration, clinical usefulness and overall accuracy into a single ensemble metric improved the efficiency of cognitive decision-making. Xgboost/Random Forest homogenous ensembling and a highly heterogeneous ensemble approach showed high performance across multifaceted aspects of ML performance and were superior to traditional recalibration methods. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data. For models to guide clinicians in individual decisions, performance exceeding these multifaceted benchmarks is necessary.

Heart failure (HF) is a leading cause of death worldwide and research focused on human cardiac tissue provides insights into underlying disease mechanisms. In this study, we performed transcriptome analyses on cardiac tissue from 21 South Asian (SA) HF patients and compared them with 21 controls without HF. The aim was to i) probe dysregulated genes and pathways in HF and ii) identify a core HF gene signature conserved across etiologies and ethnicities. Gene set enrichment analyses on dysregulated genes (DGs) showed positive enrichment of Gene Ontology terms for ECM remodelling, immune signalling and negative enrichment of terms like cardiac conduction, muscle contraction reflecting altered molecular signalling and cellular cross-talk in failing hearts. The immune cell signatures among the DGs suggest changes in immune milieu which correlates with altered immune signalling pathways. Although poor concordance in DGs across studies on HF transcriptome is well documented, evidence on stratification potential of select DGs is still preliminary. Using an independent component analysis and a machine learning based workflow, we identified gene signatures from two cohorts (SA, GSE116250) independently. We refined these into a core signature after cross validating their stratification potential and by removing the genes with low potential. The core signature effectively separated HF from control samples in SA, GSE116250 and other cohorts potentially reflecting central elements underlying end stage HF. In summary, the transcriptome analyses from SA HF cohort highlights the pathways reflecting HF pathophysiology and reiterates conserved themes across public HF cohorts.

The UK Transcatheter Aortic Valve Implantation (UK-TAVI) trial has compared transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) in patients above the age of 70 years with moderate operative risk. The primary outcome was 1-year all-cause mortality. This was a non-inferiority trial that concluded that TAVI in this cohort was non-inferior to SAVR. This appraisal reports the main findings of the study and discusses the strengths and weaknesses of the trial.

Introduction The optimal timing of coronary artery bypass grafting (CABG) operations in patients with recent acute myocardial infarction (AMI) remains unclear. Aim To assess the influence of timing on post-operative outcomes in patients undergoing CABG following AMI. Material and methods In this retrospective analysis 12,224 consecutive patients undergoing CABG were included. 2477 (20.5%) patients had a history of AMI. Based on timing, patients were divided into 3 groups: those operated within 7 days of AMI; those operated after 7 days but within 1 month; and a third group operated after 1 month but within 3 months. The 3 groups were compared in terms of baseline, intra-operative, and post-operative morbidity and mortality. Multivariate analysis was carried out to assess the independent influence of timing of CABG on outcomes. Results There was no difference in terms of previous neurological events (p = 0.554), presence of carotid artery disease (p = 0.555), prevalence of hypertension (p = 0.119), diabetes (p = 0.144), hypothyroidism (p = 0.53), chronic obstructive pulmonary disease (p = 0.079), peripheral vascular disease (p = 0.771), and impaired left ventricular function (p = 0.072). On univariate analysis, mortality risk was highest between 1 week and 1 month (p = 0.003). Multivariate analysis showed that the closer the MI and CABG duration, the higher the mortality (co-efficient -0.517; p = 0.019; odds ratio = 0.596; 95% CI: 0.388-0.917). Conclusions The duration between MI and CABG has a direct influence on outcomes after CABG. While it is clear that the longer the duration between MI and CABG, the lower the mortality risk, it is however difficult to decide on an exact cut-off time frame.

Robotic totally endoscopic techniques to perform the left internal thoracic artery to left anterior descending graft, coupled with percutaneous coronary intervention (PCI), provide the least invasive option to achieve hybrid coronary revascularization (HCR). Shorter hospital stays and reduced need for blood transfusions have been consistently being reported by several studies along with similar long-term outcomes. Considerable variations exist in the definition of HCR which can be single or two-staged with surgical revascularization being carried out after PCI or vice versa. Variations also exist with respect to usage of cardiopulmonary bypass, surgical incisions, and the use of minimally invasive robotic techniques. The different strategies of HCR do not lead to similar outcomes and the findings of one strategy cannot be extrapolated to the entire group. Studies reporting different strategies of HCR should ideally provide more granular data when reporting outcomes.

Perioperative atrial fibrillation (AF) is associated with increased mortality, morbidity, and excess healthcare costs. The objective of our study was to assess if preoperative AF in patients undergoing coronary artery bypass grafting is a predictor of operative mortality, postoperative stroke, and need for postoperative dialysis by interrogating a large registry database. We included all isolated procedures performed between February 1996 and March 2019. We used a generalized linear mixed model to assess the effect of preoperative AF on mortality stroke and the need for postoperative dialysis after adjusting for the relevant confounders derived from EuroSCORE 2. Confounders considered included age, gender, neurological dysfunction, renal dysfunction, recent myocardial infarction, pulmonary disease, unstable angina, NYHA class, pulmonary hypertension, diabetes on insulin and peripheral vascular disease, and urgency of the operation. We treated the hospital and operating consultant as random effect variables. We also performed LV function subgroup analyses to assess the effect of preoperative AF on the outcomes of interest. The incidence of pre-existent AF in the cohort of patients we analyzed (N = 356,040 patients) was 3.5% (N = 12,664). In the unadjusted baseline characteristics, preoperative AF patients had more associated comorbidities. After adjustment, preoperative AF remained a significant predictor of increased mortality (odds ratio [OR]: 1.63, confidence interval [CI] 1.48-1.79, p0.001), stroke (OR: 1.33, CI 1.16-1.54, p = 0.001), and need for renal dialysis (OR:1.61, CI 1.46-1.78, p0.001). Preoperative AF was a significant predictor of adverse outcomes in patients with moderate and good LV function but not in patients with poor LV function (EF30%). Our study suggests that preoperative AF is associated with an increased risk for perioperative mortality and stroke in patients undergoing coronary artery bypass grafting.

BACKGROUND: The Trifecta valve has been reported to have excellent hemodynamics. Controversy exists on occurrence of patient-prosthesis mismatch (PPM) and data on mid-term outcome is sparse. Health-related quality of life (HRQoL) assessment for the Trifecta valve has not been reported before. The aim of this study was to report the mid-term clinical and HRQoL outcomes in patients undergoing Trifecta valve implantation at our institution. METHODS: In this prospective, observational study, patients undergoing an aortic valve replacement (AVR) using the Trifecta valve were included. Data collection was retrospective from prospectively collected institutional database. Clinical and echocardiographic data were collected prospectively during follow-up. Quality of life was assessed using the Short Form-36 (SF-36) questionnaire. RESULTS: Forty-seven patients were included in the study of which 9 (19%) were women. Isolated AVR was carried out in 33 (70%) patients. In-hospital mortality and 30-day mortality were 1 (2.1%) and 2 (4.2%), respectively. With a mean indexed effective orifice area (iEOA) 0.96 ± 0.1, none of the patients had severe PPM. Moderate PPM was seen in 19%. The mean follow-up was 3 ± 1.7 years. The 5-year survival estimate was 83.2% in the overall cohort, 81.4% in the isolated and 87.5% in the concomitant procedure group. Freedom from re-operation and structural valve degeneration at 5 years was 95.7% and 97.8%. The mean physical health composite was 69.24 ± 2 and the mean mental health composite was 69.7 ± 25, indicating excellent mental and physical well-being among patients. CONCLUSION: The Trifecta valve provides satisfactory hemodynamics, survival and freedom from re-operation and excellent HRQoL at mid-term follow-up.

Background The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II. Method The baseline features of adult patients who underwent CABG between the years 2015 and 2021 ( n = 6703) were taken and split into training data (2015–2020; n = 5561) and validation data (2020–2021; n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error. Results The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28–0.64]) compared to 0.113 [95% CI, 0.04–0.22] for the STS score and 0.146 [95% CI, 0.06–0.31] for the EuroSCORE II ( p ≪ 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33–0.50]) vs. (0.229 [95% CI, 0.18–0.3, p Conclusion NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.

Transposition of great arteries (TGA) in association with total anomalous Pulmonary venous connection (TAPVC) is a rare anomaly, that is seldom reported in adulthood. While both the conditions are life threatening on their own, in combination they compensate for each other and can often present after weeks or months after birth. Transthoracic Echocardiography (TTE) can reliably diagnose this anomaly. Cardiac MRI can also be used to confirm the diagnosis. We describe a rare combination of TGA and TAPVC in an 18-year-old patient.

Structural valve degeneration of biological valves continues to be a persistent problem and has assumed greater importance with the advent of transcatheter valve implantation techniques. Considerable variability exists among different studies in reporting the incidence of structural valve degeneration and differentiation with nonstructural valve degeneration and bio-prosthetic valve failures is important. Moreover, the pathological basis of structural valve degeneration is still not completely understood. Theoretically, trans-catheter implanted valves may be more prone to degeneration compared to surgically implanted biological valves. Measures to reduce structural valve degeneration are imperative, irrespective of the technique of valve implantation.