Preoperative neutrophil-lymphocyte ratio and outcome from coronary artery bypass grafting

Background: An elevated preoperative white blood cell count has been associated with a worse outcome after coronary artery bypass grafting (CABG). Leukocyte subtypes, and particularly the neutrophil-lymphocyte (N/L) ratio, may however, convey superior prognostic information. We hypothesized that the N/L ratio would predict the outcome of patients undergoing surgical revascularization. Methods: Baseline clinical details were obtained prospectively in 1938 patients undergoing CABG. The differential leukocyte was measured before surgery, and patients were followed-up 3.6 years later. The primary end point was all-cause mortality. Results: The preoperative N/L ratio was a powerful univariable predictor of mortality (hazard ratio [HR] 1.13 per unit, P 3.36). Conclusion: An elevated N/L ratio is associated with a poorer survival after CABG. This prognostic utility is independent of other recognized risk factors.Peer reviewedAuthor versio

Uric acid levels and outcome from coronary artery bypass grafting

ObjectiveElevated uric acid levels have been associated with an adverse cardiovascular outcome in several settings. Their utility in patients undergoing surgical revascularization has not, however, been assessed. We hypothesized that serum uric acid levels would predict the outcome of patients undergoing coronary artery bypass grafting.MethodsThe study cohort consisted of 1140 consecutive patients undergoing nonemergency coronary artery bypass grafting. Clinical details were obtained prospectively, and serum uric acid was measured a median of 1 day before surgery. The primary end point was all-cause mortality.ResultsDuring a median of 4.5 years, 126 patients (11%) died. Mean (± standard deviation) uric acid levels were 390 ± 131 μmol/L in patients who died versus 353 ± 86 μmol/L among survivors (hazard ratio 1.48 per 100 μmol/L; 95% confidence interval, 1.25–1.74; P < .001). The excess risk associated with an elevated uric acid was particularly evident among patients in the upper quartile (≥410 μmol/L; hazard ratio vs all other quartiles combined 2.18; 95% confidence interval, 1.53–3.11; P < .001). After adjusting for other potential prognostic variables, including the European System for Cardiac Operative Risk Evaluation, uric acid remained predictive of outcome.ConclusionIncreasing levels of uric acid are associated with poorer survival after coronary artery bypass grafting. Their prognostic utility is independent of other recognized risk factors, including the European System for Cardiac Operative Risk Evaluation

Impact of Partial Root Drying and Soil Mulching on Squash Yield and Water Use Efficiency in Arid

Practical and sustainable water management systems are needed in arid regions due to water shortages and climate change. Therefore, an experiment was initiated in winter (WS) and spring (SS), to investigate integrating deficit irrigation, associated with partial root drying (PRD) and soil mulching, under subsurface drip irrigation on squash yield, fruit quality, and irrigation water use efficiency (IWUE). Two mulching treatments, transparent plastic mulch (WM) and black plastic mulch (BM), were tested, and a treatment without mulch (NM) was used as a control. Three levels of irrigation were examined in a split-plot design with three replications: 100% of crop evapotranspiration (ETc), representing full irrigation (FI), 70% of ETc (PRD70), and 50% of ETc (PRD50). There was a higher squash yield and lower IWUE in SS than WS. The highest squash yields were recorded for PDR70 (82.53 Mg ha−1) and FI (80.62 Mg ha−1). The highest IWUE was obtained under PRD50. Plastic mulch significantly increased the squash yield (34%) and IWUE (46%) and enhanced stomatal conductance, photosynthesis, transpiration, leaf chlorophyll fluorescence, and leaf chlorophyll contents under PRD plants. These results indicate that in arid and semi-arid regions, soil mulch with deficit PRD could be used as a water-saving strategy without reducing yields

N-terminal pro-B-type natriuretic peptide levels and early outcome after cardiac surgery: a prospective cohort study

Background N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a powerful predictor of cardiovascular outcome in many circumstances. There are, however, limited data regarding the utility of NT-proBNP or BNP levels in patients undergoing cardiac surgery. The current study assesses the ability of NT-proBNP to predict early outcome in this setting. Methods One thousand and ten patients undergoing non-emergent cardiac surgery were recruited prospectively. Baseline clinical details were obtained and the European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Parsonnet score were calculated. Preoperative NT-proBNP levels were measured using the Roche Elecsys assay. The primary endpoint was 30 day mortality. Results Median NT-proBNP levels were 624 ng litre−1 among patients who died within 30 days of surgery (n=29), compared with 279 ng litre−1 in survivors [odds ratio (OR) 1.03 per 250 ng litre−1, 95% confidence interval 1.01–1.05, P=0.001). NT-proBNP levels remained predictors of 30 day mortality in models including either the additive EuroSCORE (OR 1.03 per 250 ng litre−1, P=0.01), the logistic EuroSCORE (OR 1.03 per 250 ng litre−1, P=0.004), or the Parsonnet score (OR 1.02 per 250 ng litre−1, P=0.04). Levels of NT-proBNP were also predictors of prolonged (>1 day) stay in the intensive care unit (OR 1.03 per 250 ng litre−1, P1 week (OR 1.07 per 250 ng litre−1, P<0.001). They remained predictive of these outcomes in regression models that included either the EuroSCORE or the Parsonnet score and in a model that included all study variables. Conclusions NT-proBNP levels predict early outcome after cardiac surgery. Their prognostic utility is modest—but is independent of traditional indicators and conventional risk prediction scores

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world&rsquo;s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model&rsquo;s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon&rsquo;s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM &gt; SSR-LSTM &gt; ANFIS &gt; LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model&rsquo;s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station

Perioperative and long-term outcomes following aortic valve replacement: a population cohort study of 4124 consecutive patients

Objective: Because of increasing life expectancy, more patients require valve replacement for aortic stenosis. We aimed to determine perioperative and long-term outcomes, the factors associated with these and whether they have changed over time. Methods: We undertook a retrospective cohort study of all 4124 patients, who underwent isolated, primary aortic valve replacement in Scotland between April 1996 and March 2009 inclusive. Results: Annual operations increased by 68%, from 261 to 439. The overall risk of dying within 30 days, 5 years and 10 years was 3.4%, 19.9% and 38.5%, respectively. Over 10 years’ follow-up, 4.4% underwent further valve surgery, 7.9% suffered a stroke and 5.3% a myocardial infarction. Age, renal impairment and urgency were predictors of both perioperative and long-term death. Perioperative death was associated with left-ventricular impairment and long-term death with respiratory disease, diabetes and deprivation. Over the 13 years, there was an increase in median age (from 66 to 69 years, p &lt; 0.001), diabetes (from 1.9% to 12.6%, p &lt; 0.001), hypertension (from 26.4% to 56.1%, p &lt; 0.001), cerebrovascular disease (from 3.7% to 9.8%, p &lt; 0.001), respiratory disease (from 6.6% to 9.7%, p = 0.020) and previous myocardial infarction (from 0.6% to 5.8%, p &lt; 0.001), but the risk of perioperative death fell from 6.5% to 3.1% (odds ratio (OR) 0.87, 95% confidence interval (CI) 0.83, 0.92, p &lt; 0.001) per year. Conclusions: Patients undergoing aortic valve replacement have a poor risk profile. Over time, their numbers, age and co-morbidity have increased. In spite of these, there has been a significant reduction in the risk of perioperative death