Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.

METHODS

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.

FINDINGS

The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.

INTERPRETATION

Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.

FUNDING

Bill & Melinda Gates Foundation.

Retrospective analysis of pediatric sepsis and the burden of antimicrobial resistance in Duhok, Kurdistan Region of Iraq

Introduction: Sepsis is a life-threatening complication in pediatric patients. This study primarily aimed to investigate sepsis-causing bacteria and their antimicrobial resistance profile and check the change in the antimicrobial resistance trend for some selected bacteria. In addition, we evaluated the incidence of sepsis, the related mortality rate, and the effectiveness and outcome of the treatment regimes in sepsis pediatric patients. Methods: A retrospective analysis was conducted on 4-year data (2018-2021) collected from three intensive care units at the Hevi Pediatric Teaching Hospital. Sepsis screening involved clinical detection and confirmation by blood culture. Results: A total of 520 out of 1,098 (47.35%) blood samples showed positive microbial growth. A decrease in sepsis rate was observed during the COVID-19 pandemic. Coagulase-negative Staphylococci (CoNS) and Klebsiella pneumonia were the most commonly isolated bacteria. A notable variation in the antimicrobial resistance trend was observed among sepsis-causing bacteria. The empirical sepsis treatment recommended by the WHO was ineffective, as certain bacteria exhibited 100% resistance to every antibiotic tested. The mortality rate significantly increased from 1.3% in 2018 to 16.5% in 2021. Discussion: The antimicrobial resistance profile of sepsis causing bacteria is of concerns, indicating a potentially serious situation. Thus, to avoid treatment failure, the monitoring of antimicrobial resistance in pediatric patients is essential.

Factors affecting duration of stay in the intensive care unit after coronary artery bypass surgery and its impact on in-hospital mortality: a retrospective study

BACKGROUND

Different risk factors affect the intensive care unit (ICU) stay after cardiac surgery. This study aimed to evaluate these risk factors.

PATIENTS AND METHODS

A retrospective analysis was conducted on clinical, operative, and outcome data from 1070 patients (mean age: 59 ± 9.8 years) who underwent isolated coronary bypass grafting CABG surgery with cardiopulmonary bypass. The outcome variable was prolonged length of stay LOS in the CICU stay (> 3 nights after CABG).

RESULTS

Univariate predictors of prolonged ICU stays included a left atrial diameter of > 4 cm (P < 0.001),chronic obstructive airway disease COPD (P = 0.005), hypertension (P = 0.006), diabetes mellitus (P = 0.009), having coronary stents (P = 0.006), B-blockers use before surgery (either because the surgery was done on urgent or emergency basis or the patients have contraindication to B-blockers use) (P = 0.005), receiving blood transfusion during surgery (P = 0.001), post-operative acute kidney injury (AKI) (P < 0.001), prolonged inotropic support of > 12 h (P < 0.001), and ventilation support of > 12 h (P < 0.001), post-operative sepsis or pneumonia (P < 0.001), post-operative stroke/TIA (P = 0.001), sternal wound infection (P = 0.002), and postoperative atrial fibrillation POAF (P < 0.001). Multivariate regression revealed that patients with anleft atrial LA diameter of > 4 cm (AOR 2.531, P = 0.003), patients who did not take B-blockers before surgery (AOR 1.1 P = 0.011), patients on ventilation support > 12 h (AOR 3.931, P =  < 0.001), patients who developed pneumonia (AOR 20.363, P =  < 0.001), and patients who developed post-operative atrial fibrillation (AOR 30.683, P =  < 0.001) were more likely to stay in the ICU for > 3 nights after CABG.

CONCLUSION

Our results showed that LA diameter > 4 cm, patients who did not take beta-blockers before surgery, on ventilation support > 12 h, developed pneumonia post-operatively, and developed POAF were more likely to have stays lasting > 3 nights. Efforts should be directed toward reducing these postoperative complications to shorten the duration of CICU stay, thereby reducing costs and improving bed availability.

Chinese medicine, Qijudihuang pill, mediates cholesterol metabolism and regulates gut microbiota in high-fat diet-fed mice, implications for age-related macular degeneration

Background

Traditional Chinese Medicines have been used for thousands of years but without any sound empirical basis. One such preparation is the Qijudihuang pill (QP), a mixture of eight herbs, that has been used in China for the treatment of various conditions including age-related macular degeneration (AMD), the most common cause of blindness in the aged population. In order to explain the mechanism behind the effect of QP, we used an AMD model of high-fat diet (HFD) fed mice to investigate cholesterol homeostasis, oxidative stress, inflammation and gut microbiota.

Methods

Mice were randomly divided into three groups, one group was fed with control diet (CD), the other two groups were fed with high-fat-diet (HFD). One HFD group was treated with QP, both CD and the other HFD groups were treated with vehicles. Tissue samples were collected after the treatment. Cholesterol levels in retina, retinal pigment epithelium (RPE), liver and serum were determined using a commercial kit. The expression of enzymes involved in cholesterol metabolism, inflammation and oxidative stress was measured with qRT-PCR. Gut microbiota was analyzed using 16S rRNA sequencing.

Results

In the majority of the lipid determinations, analytes were elevated by HFD but this was reversed by QP. Cholesterol metabolism including the enzymes of bile acid (BA) formation was suppressed by HFD but again this was reversed by QP. BAs play a major role in signaling between host and microbiome and this is disrupted by HFD resulting in major changes in the composition of colonic bacterial communities. Associated with these changes are predictions of the metabolic pathway complexity and abundance of individual pathways. These concerned substrate breakdowns, energy production and the biosynthesis of pro-inflammatory factors but were changed back to control characteristics by QP.

Conclusion

We propose that the ability of QP to reverse these HFD-induced effects is related to mechanisms acting to lower cholesterol level, oxidative stress and inflammation, and to modulate gut microbiota.

Applying an interpretive machine learning algorithm to predict in-hospital mortality in elderly asian patients with acute coronary syndrome (ACS)

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Mosti TED1 grant

Background

No study has used interpretative Machine Learning (ML) algorithms to predict in-hospital mortality for the Asian elderly (65+). TIMI predicts mortality for STEMI and NSTEMI using two different scores. It was based on the Western cohort with limited Asian data.

Purpose

Develop a single mortality risk scoring system for STEMI and NSTEMI patients and use interpretative ML to identify and analyse risk factors in elderly Asian patients with ACS.

Methods

The National Cardiovascular Disease Database registry identified 4305 elderly. 70% of the data was used to develop algorithms and 30% for validation. Fifty-four parameters were considered, including demographics, cardiovascular risk, medications, and clinical variables. A sequential backward elimination (SBE) algorithm was used to identify variables associated to elderly mortality. XGBoost classification algorithm and SHapley Additive exPlanation (SHAP) were used to understand mortality impact. The SHAP value of each variable represents its impact on model output (mortality). The main performance metric was area under the curve (AUC). The model was validated using a validation dataset and compared to STEMI and NSTEMI for TIMI.

Results

XGBoost's validation dataset performance using the top 12 predictors from SBE for; STEMI (AUC = 0.822, 95% CI: 0.775-0.868, Accuracy: 0.875, Sensitivity: 0.164, Specificity: 0.966) and NSTEMI (AUC = 0.853, 95% CI: 0.802-0.904, Accuracy: 0.950, Sensitivity: 0.154, Specificity: 0.997). XGBoost's validation dataset performance using the eight emergency predictors selected from the top twelve predictors for; STEMI (AUC = 0.813, 95% CI: 0.766-0.861, Accuracy: 0.868, Sensitivity: 0.194, Specificity 0.954) and NSTEMI (AUC = 0.867, 95% CI: 0.812-0.921, Accuracy: 0.941, Sensitivity: 0.333, Specificity: 0.978). Both models outperformed TIMI score (STEMI AUC = 0.702, NSTEMI AUC = 0.524). The predictors were chosen and ranked in ascending order using the SHAP values (Figure 1). On the y-axis, the variable names are displayed in ascending order of importance and the colour represents the feature's value, ranging from low to high, allowing comprehension of the distribution of SHAP values for each feature. The x-axis displays the SHAP values. Eight out of the twelve predictors were identified to be emergency variables and was ranked according to SHAP values (Figure 2). When compared to TIMI, cardiac catheterization, percutaneous coronary intervention, and pharmacotherapy drugs are chosen as predictors that improve mortality prediction in STEMI and NSTEMI elderly patients. High killip class and age are linked to a poorer ACS patient survival rate, but cardiac catheterization and use of pharmacotherapy drugs improve patient mortality.

Conclusions

A single algorithm can better classify elderly ASIAN patients with ACS than TIMI, which requires two scores. The use of interpretative algorithms aids in the understanding of ACS elderly hospital mortality factors.

The Impact of COVID-19 National Lockdown on Myocardial Infarction (MI) Hospitalizations in Northern Jordan

Background

During COVID-19 lockdown periods, several studies reported decreased numbers of myocardial infarction (MI) admissions. The lockdown impact has not yet been determined in developing countries. The aim of this study was to investigate the impact that of the lockdown measures might have had on the mean number of MI hospital admissions in Northern Jordan.

Methodology

A single-center study examined consecutive admissions of MI patients during COVID-19 outbreak. Participants' data was abstracted from the medical records of King Abdullah University Hospital between 2018 and 2020. Mean and percentages of monthly admissions were compared by year and by lockdown status (pre-lockdown, lockdown, and post-lockdown time intervals).

Results

A total of 1380 participants were admitted with acute MI symptoms: 59.2% of which were STEMI. A decrease in number of MI admissions was observed in 2020, from 43.1 (SD: 8.017) cases per month in 2019 to 40.59 (SD: 10.763) in 2020 (P < 0.0001) while an increase in the numbers during the lockdown was observed. The mean number during the pre-lockdown period was 40.51 (SD: 8.883), the lockdown period was 44.74 (SD: 5.689) and the post-lockdown was 34.66 (SD: 6.026) (P < 0.0001 for all comparisons). Similar patterns were observed when percentages of admissions were used.

Conclusion

Upon comparing the lockdown period both to the pre- and post-lockdown periods separately, we found a significant increase in MI admissions during the lockdown period. This suggests that lockdown-related stress may have increased the risk of myocardial infarction.

The impact of partial and complete lockdown during the COVID-19 pandemic on acute surgical services: a retrospective cohort study

PURPOSE

The burden of the coronavirus disease of 2019 (COVID-19) pandemic on the healthcare sector has been overwhelming, leading to drastic changes in access to healthcare for the public. We aimed to establish the impact of implemented government partial and complete lockdown policies on the volume of surgical patient admissions at a tertiary referral center during the pandemic.

METHODS

A database was retrospectively created from records of patients admitted to the surgical ward through the emergency department. Three 6-week periods were examined: The complete lockdown period (CLP), which included a ban on the use of cars with the exception of health service providers and essential sector workers; A pre-COVID period (PCP) 1 year earlier (no lockdown); and a partial lockdown period (PLP) that involved a comprehensive curfew and implementing social distancing regulations and wear of personal protective equipment (e.g., masks) in public places.

RESULTS

The number of patients admitted to the surgery ward was significantly higher in the PCP cohort compared to the CLP and PLP cohorts (p = 0.009), with a 42.1% and 37% decline in patients' admissions, respectively. Admission rates for patients with biliary pathologies and vascular thrombotic events increased. 30-day mortality rates did not differ significantly between the three periods (p = 0.378).

CONCLUSIONS

While COVID-19 lockdown regulations had a significant impact on patient admission rates, surgical outcomes were not affected and the standards of care were maintained. Future protocols should strive to improve access to healthcare to avoid complications caused by delayed diagnosis and treatment.

30 days mortality prediction and risk factor analysis of Asian patients with ACS using interpretable machine learning algorithm

Background

Thrombolysis in Myocardial Infarction (TIMI) is used to predict the mortality rate in patients with acute coronary syndrome (ACS). TIMI was developed with limited data on the Asian cohort and was based on the Western cohort. STEMI and NSTEMI have separate TIMI scores. There has been limited research on Asian ACS patients using interpretable machine learning (ML) algorithms.

Purpose

To construct a single 30-day mortality risk scoring system, as well as identify and analyse risk factors in ASIAN patients with ACS, that is applicable to both STEMI and NSTEMI patients, using an interpretable ML algorithm.

Methods

The National Cardiovascular Disease Database registry data of 9054 patients was used. 70% of the data was used for algorithm development, with the remaining 30% used for validation Fifty-four parameters were considered, demographics, cardiovascular risk, medications, and clinical variables. To provide better guidance and advice for clinical judgement, the gradient boosting algorithm (XGBoost) for classification analysis and SHapley Additive exPlanation (SHAP) value analysis graphs were used. Each indicator's SHAP value indicates the impact on model output (mortality) and was calculated using the XGBoost model. The performance evaluation metric was the area under the curve (AUC). The model was validated with a validation dataset and compared to the conventional score TIMI for STEMI and NSTEMI.

Results

The performance on validation dataset of the XGBoost algorithm using the top ten predictors from SHAP for; STEMI (AUC = 0.8534, 95% CI: 0.8226–0.8842, Accuracy: 0.8053, Sensitivity: 0.73125, Specificity: 0.81355) and NSTEMI (AUC = 0.8145, 95% CI: 0.77–0.8589, Accuracy: 0.7972, Sensitivity: 0.64356, Specificity: 0.81232) outperformed TIMI score (STEMI AUC = 0.785, NSTEMI AUC = 0.543). Killip class, age, heart rate, fasting blood glucose, ACEI, creatine kinase, systolic blood pressure, HDLC, cardiac catheterization, and oralhypogly are the top ten predictors chosen by the SHAP feature selection in ascending order. Cardiac catheterization and pharmacotherapy drugs as selected predictors improve mortality prediction in STEMI and NSTEMI patients compared to TIMI. The variable names are displayed on the y-axis in ascending order of importance. The average SHAP value is shown next to them. The SHAP value is shown on the x-axis. The colour represents the value of the feature, ranging from small to large, allowing comprehension of the distribution of the SHAP values for each feature (Figure 1). We can see that having a high killip class and being older are linked to a lower survival rate in ACS patients. Cardiac catheterization procedures, as well as the use of ACEI and OHA, both improve patient mortality (Figure 2).

Conclusions

A single algorithm would classify ACS patients better than TIMI, which requires two distinct scores. In order to better predict 30-day mortality in an ASIAN population, interpretable ML can be used.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1

Molecular Characterization of Extended Spectrum β-Lactamase (ESBL) and Virulence Gene-Factors in Uropathogenic Escherichia coli (UPEC) in Children in Duhok City, Kurdistan Region, Iraq

Background: The presence of extended-spectrum β-lactamase (ESBL)-producing bacteria among uropathogens is significantly increasing in children all over the world. Thus, this research was conducted to investigate the prevalence of E. coli and their antimicrobial susceptibility pattern, and both genes of ESBL-producing E. coli resistant and virulence factor in UTIs patients among children in Duhok Province, Kurdistan, Iraq. Method: a total of 67 E. coli were identified from 260 urine samples of pediatric patients diagnosed with UTIs aged (0−15 years) which were collected from Heevi Pediatric Teaching Hospital, from August 2021 to the end of February 2022. Result: a high proportion of UPEC infections at ages <5 years and the rates among girls (88%) were significantly higher than those among the boys. A wide variety of E. coli are resistant to most antibiotics, such as Amoxicillin, Ampicillin and Tetracycline, and 64% of them were positive for ESBL. Interestingly, the presence of both the ESBL marker genes (blaTEM, and blaCTX-M) as well as both virulence marker genes (pai and hly) were detected in above 90% of E. coli. Conclusion: the data illustrate an alarming increase in UPEC with ESBL production and the emergence of multidrug-resistant drugs in the early age of children. The public health sectors should further monitor the guidelines of using antibiotics in Kurdistan, Iraq.

Knowledge and attitudes amongst internal medicine clinicians in referring valvular heart disease patients : a multi-centre survey study

Funding Acknowledgements

Type of funding sources: None.

Background

With advancement in valvular interventions, outcomes of valvular heart disease (VHD) patients have improved dramatically. However, very little is known regarding levels of knowledge and attitudes amongst non-cardiology clinicians in managing VHD. 

Purpose

To determine the knowledge and attitudes towards VHD management, pertaining specifically to referrals to cardiology services for consideration for intervention, amongst internal medicine clinicians in Malaysia.

Methods

We conducted a multi-centre survey study. A 20-item survey was distributed amongst internal medicine clinicians within three different institutions.

Results

A total of 75 responses was obtained (response rate of 35.7%). The mean age of responders was 36.6 years (S.D. 7.9). 57.3% were male. Respondents include medical officers (50.6%), internal medicine consultants (31.9%) and internal medicine specialist (i.e. pre-consultant) (17.5%). Majority had more than 10 years of experience working as medical doctors (46.7%). 69.3% admit to managing patients with VHD regularly (defined as at least one VHD patient per week). Respondents were comfortable using online resources, both formal (95.0%) and informal (75.0%) sources. A large proportion felt that they had ‘insufficient’ or ‘very insufficient’ knowledge and experience managing VHD patients on their own (28.0% and 8.0% respectively) and less than half felt that management of VHD patients within their institutions was ‘adequate’ or ‘very adequate’ (46.7% and 2.7% respectively). However, it is reassuring that most respondents felt comfortable discussing cases of VHD with their senior colleagues or their local cardiology services. Although many were felt to be over-zealous in wanting too frequent an echocardiography monitoring for cases of mild VHD (majority felt that the patient would require it every 1 year (25.3%) or 1-2 years (29.3%)), nevertheless attitudes in referrals to cardiology services for consult and intervention were felt appropriate (majority felt that cardiology consult would be indicated if valvular lesion was at least moderate in severity (56.0%), when there were changes to the heart function or structure (52.0%), when patients became symptomatic (45.3%) or when there were two or more valvular lesions simultaneously (45.3%). However, within a Malaysian context, there may be a need to shift attitudes towards referring much earlier, consistent with changes in recent international guidelines on managing VHD, as valvular surgery waiting times are extremely long.

Conclusion

The survey highlights the need for education amongst internal medicine clinician to tackle both the over-zealous attitude in monitoring patients with mild valvular disease on one extreme,  and in improving early referrals for VHD indicated for intervention in the other. Abstract Figure. Survey items on echocardiography  Abstract Figure. Survey items on cardiology referrals

Machine learning to predict in-hospital mortality risk among heterogenous STEMI patients with diabetes

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): TECHNOLOGY DEVELOPMENT FUND 1

Background

Diabetes has become a major public health concern in Asia. In Malaysia, the prevalence of diabetes has escalated in adults above the age of 18, affecting 3.9  million individuals. Patients with diabetes and coronary heart disease have worse outcomes, compared with patients without diabetes who have coronary heart disease. Conventional Risk scores such as TIMI and GRACE were derived from a Western Caucasian cohort with limited data from  Asian countries, despite Asia hosting 60% of the world’s population.

Purpose

It is important to recognize the significant features associated with in-hospital mortality risk that is population-specific in Asian diabetes patients with STEMI to achieve a reliable and effective clinical diagnosis and improved outcome. Electronic health records contain large amounts of information on patients’ medical history and are becoming invaluable research tools that could be applied to cardiovascular disease risk prediction through machine learning (ML) algorithms. With the current success of ML  over conventional methods in STEMI mortality prediction, we aim to develop ML algorithms for in-hospital risk mortality in Asian patients diagnosed with DM that can be adopted for clinical predictions

Methods

We used registry data from the Malaysian National Cardiovascular Disease Database of 5783 patients diagnosed with DM from 2006 to 2016. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. Four machine learning (ML) algorithms were constructed using a 70% registry dataset; Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Booster (XGB) and Logistic Regression (LR). Feature selections were done based on ML algorithms feature importance combined with Sequential Backward Selection (SBS). The area under the curve (AUC)  was used as the performance evaluation metric. All algorithms were validated using a 30 % validation dataset and compared to the conventional TIMI risk score for STEMI.

Results

The best model SVM (AUC = 0.90) outperformed other ML algorithms (Figure 1) and TIMI risk score (AUC = 0.83). The best SVM model consists of 11 predictors which are Killip class, fasting blood glucose, age, systolic blood pressure, heart rate, ACE inhibitor, beta-blocker, total cholesterol, diastolic blood pressure, lower density lipoprotein, and diuretic (Figure 2). Common predictors of SVM and TIMI risk score are Killip class, age, systolic blood pressure, and heart rate. We have shown that the population-specific data mining approach for the prediction of diabetes patients’ mortality post-STEMI outperformed conventional TIMI risk score.

Conclusion

In the Asian multiethnic population, combination of ML approaches with features selection demonstrated promising outcomes in patients with DM that may be used for better patient prognostic than the conventional method. Abstract Figure 1: ML Best Model Performance  Abstract Figure 2: Selected Predictors for ML

Knowledge and attitudes amongst internal medicine clinicians in referring valvular heart disease patients : a multi-centre survey study

Funding Acknowledgements

Type of funding sources: None.

Background

With advancement in valvular interventions, outcomes of valvular heart disease (VHD) patients have improved dramatically. However, very little is known regarding levels of knowledge and attitudes amongst non-cardiology clinicians in managing VHD. 

Purpose

To determine the knowledge and attitudes towards VHD management, pertaining specifically to referrals to cardiology services for consideration for intervention, amongst internal medicine clinicians in Malaysia.

Methods

We conducted a multi-centre survey study. A 20-item survey was distributed amongst internal medicine clinicians within three different institutions.

Results

A total of 75 responses was obtained (response rate of 35.7%). The mean age of responders was 36.6 years (S.D. 7.9). 57.3% were male. Respondents include medical officers (50.6%), internal medicine consultants (31.9%) and internal medicine specialist (i.e. pre-consultant) (17.5%). Majority had more than 10 years of experience working as medical doctors (46.7%). 69.3% admit to managing patients with VHD regularly (defined as at least one VHD patient per week). Respondents were comfortable using online resources, both formal (95.0%) and informal (75.0%) sources. A large proportion felt that they had ‘insufficient’ or ‘very insufficient’ knowledge and experience managing VHD patients on their own (28.0% and 8.0% respectively) and less than half felt that management of VHD patients within their institutions was ‘adequate’ or ‘very adequate’ (46.7% and 2.7% respectively). However, it is reassuring that most respondents felt comfortable discussing cases of VHD with their senior colleagues or their local cardiology services. Although many were felt to be over-zealous in wanting too frequent an echocardiography monitoring for cases of mild VHD (majority felt that the patient would require it every 1 year (25.3%) or 1-2 years (29.3%)), nevertheless attitudes in referrals to cardiology services for consult and intervention were felt appropriate (majority felt that cardiology consult would be indicated if valvular lesion was at least moderate in severity (56.0%), when there were changes to the heart function or structure (52.0%), when patients became symptomatic (45.3%) or when there were two or more valvular lesions simultaneously (45.3%). However, within a Malaysian context, there may be a need to shift attitudes towards referring much earlier, consistent with changes in recent international guidelines on managing VHD, as valvular surgery waiting times are extremely long.

Conclusion

The survey highlights the need for education amongst internal medicine clinician to tackle both the over-zealous attitude in monitoring patients with mild valvular disease on one extreme,  and in improving early referrals for VHD indicated for intervention in the other. Abstract Figure. Survey item on echocardiography  Abstract Figure. Survey item on cardiology referrals

ACS mortality prediction in Asian in-hospital patients with deep learning using machine learning feature selection

Background

Thrombolysis in Myocardial infarction (TIMI) is used in predicting the mortality rate of the acute coronary syndrome (ACS) patients. TIMI was developed based on the Western cohort with limited data on the Asian cohort. There are separate TIMI scores for STEMI and NSTEMI. Deep learning (DL) and machine learning (ML) algorithms such as support vector machine (SVM) in population-specific dataset resulted in a higher area under the curve (AUC) to TIMI. The limitation of DL is selected features by the algorithm is unknown compared to ML algorithms.

Purpose

To construct a single in-hospital mortality risk scoring system that combines SVM feature importance and the DL algorithm in ASIAN patients with ACS that is applicable for both STEMI and NSTEMI patients. To investigate DL performance constructed using predictors selected from SVM feature extraction and DL using complete features and compare with TIMI risk score for STEMI and NSTEMI patients.

Methods

We constructed four algorithms: i) DL and SVM algorithm with feature selected from SVM variable importance, ii) DL and SVM algorithm without feature selection. SVM feature importance with the backward elimination method is used to select and rank important variables. We used registry data from the National Cardiovascular Disease Database of 13190 patient's data. Fifty-four parameters including demographics, cardiovascular risk, medications and clinical variables were considered. AUC was used as the performance evaluation metric. All algorithms were validated using validation dataset and compared to the conventional TIMI for STEMI and NSTEMI.

Results

Validation results in Figure 1 are by STEMI and NTEMI patients. Both DL algorithms outperformed ML and TIMI score on validation data. Similar performance is observed for DL and SVM algorithms using all predictors (54 predictors) with DL and SVM algorithm using selected predictors (14 predictors). Predictors selected by the SVM feature selection are: age, heart rate, Killip class, fasting blood glucose, ST-elevation, CABG, cardiac catheterization, angina episode, HDLC, LDC, other lipid-lowering agents, statin, anti-arrhythmic agent, oralhypogly. CABG and pharmacotherapy drugs as selected predictors improve mortality prediction compared to TIMI score. In DL, 25.87% of STEMI patients and 19.71% of NSTEMI patients are estimated as high risk (risk probabilities of &gt;50%). TIMI underestimated the risk of mortality of high-risk patients (≥5 risk scores) with 13.08% from STEMI patients and 4.65% from NSTEMI patients (Figure 2).

Conclusions

In the ASIAN multi-ethnicity population, patients with ACS can be better classified using one single algorithm compared to the conventional method like TIMI which requires two different scores. Combining ML feature selection with DL allows the identification of distinct factors related to in-hospital mortality of ACS patients in a unique ASIAN population for better mortality prediction.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 Figure 1. Performance resultsFigure 2. Analysis on the validation set

Investigating performance of deep learning and machine learning risk stratification of Asian in-hospital patients after ST-elevation myocardial infarction

Background

Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management.

Purpose

To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors.

Methods

We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables.

Results

DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities &gt;50%) compared to only 12.7% nonsurvival patients by TIMI (score &gt;5) (Figure below).

Conclusions

In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Transcriptome-wide changes associated with the reproductive behaviour of male guppies exposed to 17α-ethinyl estradiol

Although many pharmaceutical compounds (and their metabolites) can induce harmful impacts at the molecular, physiological and behavioural levels, their underlying mechanistic associations have remained largely unexplored. Here, we utilized RNA-Seq to build a whole brain transcriptome profile to examine the impact of a common endocrine disrupting pharmaceutical (17α-ethinyl estradiol, EE2) on reproductive behaviour in wild guppies (Poecilia reticulata). Specifically, we annotated 16,791 coding transcripts in whole brain tissue in relation to the courtship behaviour (i.e. sigmoid display) of EE2 exposed (at environmentally relevant concentration of 8 ng/L for 28-days) and unexposed guppies. Further, we obtained 10,960 assembled transcripts matching in the non-coding orthologous genomes. Behavioural responses were assessed using a standard mate choice experiment, which allowed us to disentangle chemical cues from visual cues. We found that a high proportion of the RNAseq reads aligned back to our de novo assembled transcriptome with 80.59% mapping rate. Behavioural experiments showed that when males were presented only with female visual cues, there was a significant interaction between male treatment and female treatment in the time spent in the preference zone. This is one of the first studies to show that transcriptome-wide changes are associated with the reproductive behaviour of fish: EE2 exposed male guppies that performed high levels of courtship had a gene profile that deviated the most from the other treatment groups, while both non-courting EE2 and control males had similar gene signatures. Using Gene Ontology pathway analysis, our study shows that EE2-exposed males had gene transcripts enriched for pathways associated with altered immunity, starvation, altered metabolism and spermatogenesis. Our study demonstrates that multiple gene networks orchestrate courting behaviour, emphasizing the importance of investigating impacts of pharmaceuticals on gene networks instead of single genes.

Predictors of short-term mortality after rheumatic heart valve surgery: A single-center retrospective study

Background

Valve replacement surgeries holds risks of morbidity and mortality.

Materials and methods

The study cohort included 346 patients who underwent different types of valve surgery, excluding redo and Bentall operations. All operations were performed through a median sternotomy using cardiopulmonary bypass.

Results

Mean patient age was 51.6 ± 16.1 years, and 51% were male. Approximately 21% had diabetes, and 44.6% were hypertensive. Aortic valve replacement (AVR) was performed in 125 patients (37%), mitral valve replacement (MVR) in 95 (28%), combined AVR and MVR in 42 (13%), AVR plus coronary artery bypass grafting (CABG) in 19 (6%), and MVR plus CABG in 32 (10%). Operative mortality was 5.8% (n = 20). In the bivariate-level analysis, older age, operation type, hypertension, emergency surgery, use of a biological valve in the aortic or mitral position, pump time greater than 120 min, and aortic clamp time greater than 60 min were significant predictors of 30-day mortality. Use of medications stratified by duration (less than or more than a month) was also shown to be a predictor of mortality. Use of angiotensin-converting enzyme inhibitors, digoxin, beta-blockers, statins, and loop diuretics was associated with mortality. Older age, emergency/salvage surgery, use of beta-blockers for less than 1 month preoperatively, and use of a biological valve in the aortic position were significant and independent predictors of 30-day mortality.

Conclusion

Age, emergency valve surgery, use of a biological valve, use of beta-blockers for less than 1 month before surgery, type of surgery, EF<35%, pump time, and cross clamp time were all found to be independent predictors of mortality in patients undergoing valve surgery. Further prospective multicenter studies may be needed to provide a comprehensive assessment of mortality in patients undergoing valve surgery in Jordan.

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Rheumatic heart disease is still a health issue in developing countries.

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Developed countries are facing rheumatic patients due to forced immigrations.

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Early diagnosis and early referral to surgery is correlated with better outcomes.

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More than eight predictors were found to be independent predictors of mortality.

Lesion Type Analysis of Hemodialysis Patients Who Underwent Endovascular Management for Symptomatic Central Venous Disease

Purpose

Central venous lesions (CVLs) can adversely affect hemodialysis access maturation and maintenance, which in turn worsen patient morbidity and access circuit patency. In this study, we assessed several clinical variables, patient characteristics, and clinical consequences of symptomatic central vein stenosis and obstruction in patients who underwent renal replacement therapy in the form of hemodialysis.

Patients and Methods

The medical records of all hemodialysis patients with clinically symptomatic CVLs who underwent digital subtraction angiography treatment at King Abdullah University Hospital between January 2017 and December 2019 were retrieved. Patient characteristics and the clinical and anatomical features of CVLs were analyzed retrospectively. Pearson’s chi-square tests of association were used to identify and assess relationships between patient characteristics and CVLs.

Results

The study cohort comprised 66 patients with end-stage renal disease who developed symptomatic central vein stenosis. Of the 66 patients, 56.1% were men, and their mean age was approximately 52 years. Most (62.1%) of the patients were determined to have a history of central catheter insertion into the jugular vein. Hypertension was the most common comorbidity (78.8%, p<0.001), followed by type 2 diabetes mellitus (47.0 %, p<0.01). The incidence of stenosis was found to be significantly higher in the brachiocephalic vein than in other central veins (43.9%, p<0.001). A repeated central catheter insertion in a patient was predictive of central venous occlusion (p<0.05). Stenotic lesions were found to be associated with a significantly higher success rate than occlusive lesions (91.2%, p<0.01).

Conclusion

Multiple central venous catheters (CVCs) are found to be associated with occlusive CVLs and unfavorable recanalization outcomes. Multiple CVC should be avoided by creating a permanent vascular access in a timely fashion for patients with chronic kidney disease and by avoiding the ipsilateral insertion of CVC and AVF.

Characterisation of gut microbiota of obesity and type 2 diabetes in a rodent model

Various studies have suggested that the gut microbiome interacts with the host and may have a significant role in the aetiology of obesity and Type 2 Diabetes (T2D). It was hypothesised that bacterial communities in obesity and T2D differ from control and compromise normal interactions between host and microbiota. Obesity and T2D were developed in rats by feeding a high-fat diet or a high-fat diet plus a single low-dose streptozotocin administration, respectively. The microbiome profiles and their metabolic potentials were established by metagenomic 16S rRNA sequencing and bioinformatics. Taxonomy and predicted metabolism-related genes in obesity and T2D were markedly different from controls and indeed from each other. Diversity was reduced in T2D but not in Obese rats. Factors likely to compromise host intestinal, barrier integrity were found in Obese and T2D rats including predicted, decreased bacterial butyrate production. Capacity to increase energy extraction via ABC-transporters and carbohydrate metabolism were enhanced in Obese and T2D rats. T2D was characterized by increased proinflammatory molecules. While obesity and T2D show distinct differences, results suggest that in both conditions Bacteroides and Blautia species were increased indicating a possible mechanistic link.

COVID-19: an update and cardiac involvement

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through angiotensin converting enzyme 2 receptors, leading to coronavirus disease (COVID-19)-related pneumonia, and also causing acute cardiac injury and chronic damage to the cardiovascular system. The purpose of this review is primarily reviewing the COVID-19 disease, including pathogen, clinical features, diagnosis, and treatment with particular attention to cardiovascular involvement based on the current evidence. COVID-19 remains a threat to global public health. The associated extra-pulmonary manifestations and their prolonged consequences are frequently overlooked. Pre-existing cardiovascular disease or acute cardiac complications may contribute to adverse early clinical outcome. At the moment, there is no specific treatment for COVID-19, but multiple randomized controlled trials (RCT) are being conducted. New supportive therapies are being evaluated with promising results.

Predictors of Acute Kidney Injury Following Surgical Valve Replacement

BACKGROUND

 Acute kidney injury is a serious complication after surgical valve replacement and holds increased mortality rates.

OBJECTIVES

 To study predictors of acute kidney injury after surgical valve replacement.

MATERIALS AND METHODS

 Patients who underwent valve surgery procedures at our center were included. Procedures included aortic valve replacement (AVR), mitral valve replacement (MVR), AVR with coronary artery bypass grafting (CABG), MVR with CABG, or AVR and MVR with/without CABG.

RESULTS

 A total of 346 patients were included. The mean age was 51.56 (16.1). Males (n = 178) comprised 51%.At the univariate level analysis, predictors of acute kidney injury were found including age, ejection fraction, hypertension, history of CAD, emergency surgery, recent myocardial infarction, diabetes, atrial fibrillation, history of heart failure, mitral regurgitation (MR), pump time >120 minutes, aortic cross clamp >90 minutes, perioperative blood transfusion, re-exploration for bleeding, use of mechanical and biologic valve in aortic position, use of biologic valve in mitral position, prolonged inotropic support, postoperative stroke, and use of angiotensin converting enzyme inhibitors (ACEi) < a month, (all p < 0.05).By Logistic regression analysis, Age (p < 0.0001, odds ratio[AOR] = 1.076), hypertension (p = 0.039, AOR = 1.829), heart failure (p = 0.019, AOR = 2.448), MR (p = 0.0001, AOR = 3.110), use of ACEi <month (p = 0.043, AOR= 2.181), pump time >120 minutes (p = 0.022, AOR = 1.797), perioperative blood transfusion (p = 0.008, AOR = 2.532), and prolonged inotropic support (p = 0.012, AOR = 2.591) were significant and independent predictors of AKI.

CONCLUSION

 Independent predictors of acute kidney injury following valve surgeries include age, hypertension, heart failure, MR, use of ACEi <month, perioperative blood transfusion, and prolonged pump time or inotropic support.

Coronary artery bypass as a treatment of stent dislodgement: A case report

Introduction

Stent dislodgement is a known complication during coronary angiography. Different methods are used to retrieve it including open heart surgery.

Case presentation

A 71 year-old male with stable angina was scheduled for elective coronary angiography. Angiography showed two significant stenosis: one in the proximal right coronary artery (RCA) and one in the left anterior descending artery (LAD). Upon deployment of the right coronary stent, it got lodged and the cardiologist was unable to retrieve it. The patient started to experience angina and his ECG showed ST segment elevation in the inferior leads. Emergency CABG was performed.

Conclusion

Stent dislodgement is a rare but serious complication. Most cases are treated by interventional methods; however, CABG is still needed in some cases.

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Stent dislodgement is not a common complication of coronary angiogram.

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Dislodged stents are usually removed by catheterization.

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Coronary artery bypass surgery CABG is needed if retrieval is not successful.

Level and significance of plasma myeloperoxidase and the neutrophil to lymphocyte ratio in patients with coronary artery disease

Inflammation plays a pivotal role in the etiology of coronary artery disease (CAD). Myeloperoxidase (MPO) is a potent inflammatory factor and a critical modulator of coronary inflammation and oxidative stress. The goal of this study was to determine the impact of the plasma MPO (pMPO) level and neutrophil/lymphocyte ratio on the clinical characteristics and outcomes of patients with CAD. Blood samples were collected from 210 patients with underlying chest pain or recent myocardial infarction (MI) prior to coronary angiography in order to measure pMPO levels. The pMPO levels and neutrophil/lymphocyte ratio were correlated with clinical characteristics and outcomes following catheterization. The pMPO level and neutrophil/lymphocyte ratio were higher in patients with recent MI than in patients with CAD (coronary occlusion ≥50%) or without CAD (coronary occlusion <50%). Patients with ST segment elevated MI (STEMI) had a higher neutrophil/lymphocyte ratio relative to patients with non-STEMI. The pMPO level was identified to correlate with the neutrophil/lymphocyte ratio and the need for coronary artery reperfusion by coronary artery bypass surgery or percutaneous coronary intervention. Patients who were taking aspirin had lower pMPO levels and neutrophil/lymphocyte ratio compared with those who were not taking aspirin. The plasma neutrophil/lymphocyte ratio was negatively associated with the left ventricular ejection fraction at baseline and the 30-day follow-up, whereas pMPO showed no correlation. Multivariate analysis indicated that the pMPO level was positively associated with MI, the neutrophil/lymphocyte ratio and coronary intervention. The preoperative use of aspirin was associated with a lower pMPO level and neutrophil/lymphocyte ratio. In conclusion, pMPO is positively associated with MI, the neutrophil/lymphocyte ratio and coronary intervention. The preoperative use of aspirin is associated with a lower pMPO level and neutrophil/lymphocyte ratio. pMPO may serve as a predictor of coronary intervention and as a potential therapeutic target for the reduction of inflammation in patients with CAD.

Stroke after coronary artery surgery: a single center report

Stroke or cerebrovascular accident (CVA) is a devastating complication of coronary surgery. In this report, the incidence, and correlates of CVA following isolated coronary artery bypass grafting (CABG) surgery were evaluated. Data were collected retrospectively. Between 2006 and 2009, 855 patients underwent isolated CABG surgery. CVA was defined as any new neurological deficit lasting more than 24 hours. Univariate and multivariate analyses were utilized as appropriate. The incidence of CVA was 1.4% (n = 12). Age, previous CVA, and emergency surgery were correlated by univariate analysis. Multivariate analysis revealed age, previous CVA, and chronic renal impairment as predictors of CVA. Ten (83.3%) of the 12 patients were diagnosed to have CVA in the first 24 hours. Length of hospital stay was 20.9 ± 20.34 days for CVA patients and 9.2 ± 5.17 days for non-CVA patients (p ≤ 0.001). There were 4 (33.3%) deaths in CVA group and 27 (3.2%) for non-CVA patients (p = 0.001). Postoperative CVA is a major contributor to mortality, prolonged hospitalization, and other adverse postoperative complications. Further studies are needed to develop better strategies to minimize the occurrence of CVA among patients undergoing CABG.

Enhanced intra-operative grading of ascending aorta atheroma by epiaortic ultrasound vs echocardiography

AIMS

Intra-operative grading of atheromatous plaques in the ascending aorta by epiaortic ultrasound (EAU) and transesophageal echocardiography (TEE) in patients who have undergone CABG.

METHODS AND RESULTS

Sixty patients scheduled for elective CABG were prospectively enrolled to undergo intra-operative TEE and EAU ultrasound scanning of the ascending aorta. The ascending aorta was divided into three sections; proximal, middle and distal, and four segments; anterior, posterior, medial and lateral. Degree of atherosclerosis was graded according to a modified Montgomery scale. Epiaortic ultrasound was unable to provide images for a reliable assessment in 56 areas (7.7%; 56/720) vs 322 non-visualized areas by TEE (44.7%; 298/720) (p<0.01). Out of 563 areas that scored >or=2, EAU visualized 379/720 areas (52.6%), whereas TEE visualized 184/720 areas (25.5%) (p<0.01). EAU mean scores were significantly higher for the mid (p=0.0001) and distal (p=0.05) sections and for the posterior segment (p<0.01) vs TEE. TEE had a higher mean score than EAU in the anterior segment. When all EAU areas were grouped the posterior segment reached a significantly higher mean score (p<0.01), and the anterior segment was the second mostly diseased.

CONCLUSIONS

EAU is the intra-operative investigation of choice because it allowed a detailed grading of atheromatous lesions over the entire length of the ascending aorta. Accurate grading by TEE was restricted only to those areas that could be sufficiently visualized. TEE has a reduced power of investigation that limits its use, especially in the distal ascending aorta, a site of great surgical manipulation.

Effect of the cardiac cycle on the coronary anastomosis assessed by ultrasound

Anastomosis of the left internal mammary artery to the left anterior descending artery was performed in 9 pigs to evaluate the effect of changes in the cardiac cycle and the choice of ultrasound mode on assessment of graft morphology. The length of the anastomosis and the diameters of the left anterior descending artery at the toe and heel of the anastomosis, as well as downstream, were measured in end-systole and end-diastole with both B-mode and color Doppler imaging. None of the diameters were influenced by the cardiac cycle using either ultrasound mode. B-mode yielded larger diameters at all points except the toe of the anastomosis. It was concluded that provided the scanning is perpendicular to the vessel, the morphology of an anastomosis can be assessed without paying much attention to the phase of the cycle or the mode of ultrasound applied.

A modified tetrazolium reaction for identifying malignant cells from gastric and colonic cancer

When non-malignant cells were reacted for glucose-6-phosphate dehydrogenase activity, with neotetrazolium chloride as the indicator of the activity, oxygen competed with the neotetrazolium and nullified the reaction. In contrast, about 30% of the activity was retained in malignant cells, in sections and in smears, from cancer of the stomach or colon. This could provide the basis of a qualitative (black-or-white) functional test for distinguishing malignant cells in these conditions.

LDH in gastric juice and its limitation in the diagnosis of gastric cancer

LDH activity in gastric juice was high in all cases of carcinoma of the stomach investigated provided that the pH of the resting gastric juice was above 5·0. No LDH activity could be detected in gastric aspirate, whether from patients with carcinoma of the stomach or not, when the pH of the resting juice was below 5·0. LDH activity in gastric juice is rapidly and irreversibly inactivated by hydrochloric acid at a pH of 4·5 to 5·0.