The fungal intestinal microbiota predict the development of bronchopulmonary dysplasia in very low birthweight newborns.

RATIONALE: Bronchopulmonary dysplasia (BPD) is the most common morbidity affecting very preterm infants. Gut fungal and bacterial microbial communities contribute to multiple lung diseases and may influence BPD pathogenesis. METHODS: We performed a prospective, observational cohort study comparing the multikingdom fecal microbiota of 144 preterm infants with or without moderate to severe BPD by sequencing the bacterial 16S and fungal ITS2 ribosomal RNA gene. To address the potential causative relationship between gut dysbiosis and BPD, we used fecal microbiota transplant in an antibiotic-pseudohumanized mouse model. Comparisons were made using RNA sequencing, confocal microscopy, lung morphometry, and oscillometry. RESULTS: We analyzed 102 fecal microbiome samples collected during the second week of life. Infants who later developed BPD showed an obvious fungal dysbiosis as compared to infants without BPD (NoBPD, p = 0.0398, permutational multivariate ANOVA). Instead of fungal communities dominated by Candida and Saccharomyces, the microbiota of infants who developed BPD were characterized by a greater diversity of rarer fungi in less interconnected community architectures. On successful colonization, the gut microbiota from infants with BPD augmented lung injury in the offspring of recipient animals. We identified alterations in the murine intestinal microbiome and transcriptome associated with augmented lung injury. CONCLUSIONS: The gut fungal microbiome of infants who will develop BPD is dysbiotic and may contribute to disease pathogenesis.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

Network Analysis of Postoperative Surgical Complications in a Cohort of Children Reported to the National Surgical Quality Improvement Program: Pediatric.

OBJECTIVE: To understand the temporal relationships of postoperative complications in children and determine if they are related to each other in a predictable manner. SUMMARY OF BACKGROUND DATA: Children with multiple postoperative complications have increased suffering and higher risk for mortality. Rigorous analysis of the temporal relations between complications, how complications might cluster, and the implications of such clusters for children have not been published. Herein, we analyze the relationships between postoperative complications in children. METHODS: Data source: Surgical operations included in the National Surgical Quality Improvement Program Pediatric Participant Use Data File from 2013 to 2017. The main outcomes measure was presence of 1 or more postoperative complications within 30 days of surgery. Operations followed by multiple complications were analyzed using network analysis to study prevalence, timing, and co-occurrences of clusters of complications. RESULTS: This study cohort consisted of 432,090 operations; 388,738 (89.97%) had no postoperative complications identified, 36,105 (8.35%) operations resulted in 1 postoperative complication and 7247 (1.68%) operations resulted in 2 or more complications. Patients with multiple complications were more likely to be younger, male, African American, with a higher American Society of Anesthesiologists score, and to undergo nonelective operations (P < 0.001). More patients died with 2 complication versus 1 complication vs no complication (5.3% vs 1.5% vs 0.14%, P < 0.001). Network analysis identified 4 Louvain clusters of complications with dense intracluster relationships. CONCLUSIONS: Children with multiple postoperative complications are at higher risk of death, than patients with no complication, or a single complication. Multiple complications are grouped into defined clusters and are not independent.

Trends in cardiology and oncology artificial intelligence publications.

Study objective: To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/setting: To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. Results: Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on "machine learning" (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to "artificial intelligence" (n = 4535), "natural language processing" (n = 2608), and "deep learning" (n = 4459). Conclusion: Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Artificial Intelligence-Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors.

PURPOSE: Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline-recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS: Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS: The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION: AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery.

Background: Scientific evidence confirm that significant racial disparities exist in healthcare, including surgery outcomes. However, the causal pathway underlying disparities at preoperative physical condition of children is not well-understood. Objectives: This research aims to uncover the role of socioeconomic and environmental factors in racial disparities at the preoperative physical condition of children through multidimensional integration of several data sources at the patient and population level. Methods: After the data integration process an unsupervised k-means algorithm on neighborhood quality metrics was developed to split 29 zip-codes from Memphis, TN into good and poor-quality neighborhoods. Results: An unadjusted comparison of African Americans and white children showed that the prevalence of poor preoperative condition is significantly higher among African Americans compared to whites. No statistically significant difference in surgery outcome was present when adjusted by surgical severity and neighborhood quality. Conclusions: The socioenvironmental factors affect the preoperative clinical condition of children and their surgical outcomes.

ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure.

AIMS: Heart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. METHODS AND RESULTS: Data from the baseline visits (1987-89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age ± standard deviation of 54 ± 5) participants were eligible. A total of 803 (5.5%) participants developed HF within 10 years from baseline. Convolutional neural network utilizing solely ECG achieved an AUC of 0.756 (0.717-0.795) on the hold-out test data. ARIC and Framingham Heart Study (FHS) HF risk calculators yielded AUC of 0.802 (0.750-0.850) and 0.780 (0.740-0.830). The highest AUC of 0.818 (0.778-0.859) was obtained when ECG-AI model output, age, gender, race, body mass index, smoking status, prevalent coronary heart disease, diabetes mellitus, systolic blood pressure, and heart rate were used as predictors of HF within LGBM. The ECG-AI model output was the most important predictor of HF. CONCLUSIONS: ECG-AI model based solely on information extracted from ECG independently predicts HF with accuracy comparable to existing FHS and ARIC risk calculators.

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients. METHODS: The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis. RESULTS: In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-0.73), random forest decision trees (area under the curve 0.70; 95% confidence interval, 0.68-0.73), and extreme gradient boosting (area under the curve 0.70; 95% confidence interval, 0.68-0.73) afforded similar performance, while support vector machines (area under the curve 0.51; 95% confidence interval, 0.50-0.52) had worse performance. Variable importance analyses identified preoperative congestive heart failure, transfusion, and acute renal failure as predictors of postoperative sepsis. CONCLUSION: Machine learning methods can be used to predict the development of sepsis after appendectomy with moderate accuracy. Such predictive modeling has potential to ultimately allow for preoperative recognition of patients at risk for developing postoperative sepsis after appendectomy thus facilitating early intervention and reducing morbidity.

The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease.

PURPOSE OF REVIEW: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). However, traditional CVD risk prediction equations do not work well in patients with CKD, and inclusion of kidney disease metrics such as albuminuria and estimated glomerular filtration rate have a modest to no benefit in improving prediction. RECENT FINDINGS: As CKD progresses, the strength of traditional CVD risk factors in predicting clinical outcomes weakens. A pooled cohort equation used for CVD risk prediction is a useful tool for guiding clinicians on management of patients with CVD risk, but these equations do not calibrate well in patients with CKD, although a number of studies have developed modifications of the traditional equations to improve risk prediction. The reason for the poor calibration may be related to the fact that as CKD progresses, associations of traditional risk factors such as BMI, lipids and blood pressure with CVD outcomes are attenuated or reverse, and other risk factors may become more important. SUMMARY: Large national cohorts such as the US Veteran cohort with many patients with evolving CKD may be useful resources for the developing CVD prediction models; however, additional considerations are needed for the unique composition of patients receiving care in these healthcare systems, including those with multiple comorbidities, as well as mental health issues, homelessness, posttraumatic stress disorders, frailty, malnutrition and polypharmacy. Machine learning over conventional risk prediction models may be better suited to handle the complexity needed for these CVD prediction models.

Predialysis coronary revascularization and postdialysis mortality.

Objectives: Coronary artery bypass grafting (CABG) is associated with better survival than percutaneous coronary intervention (PCI) in patients with mild-to-moderate chronic kidney disease (CKD) and End-Stage Renal Disease (ESRD). However, the optimal strategy for coronary artery revascularization in advanced CKD patients who transition to ESRD is unclear. Methods: We examined a contemporary national cohort of 971 US veterans with incident ESRD, who underwent first CABG or PCI up to 5 years prior to dialysis initiation. We examined the association of a history of CABG versus PCI with all-cause mortality following transition to dialysis, using Cox proportional hazards models adjusted for time between procedure and dialysis initiation, socio-demographics, comorbidities and medications. Results: 582 patients underwent CABG and 389 patients underwent PCI. The mean age was 66±8 years, 99% of patients were male, 79% were white, 19% were African Americans, and 84% were diabetics. The all-cause post-dialysis mortality rates after CABG and PCI were 229/1000 patient-years (PY) [95% CI: 205-256] and 311/1000PY [95% CI: 272-356], respectively. Compared to PCI, patients who underwent CABG had 34% lower risk of death [multivariable adjusted Hazard Ratio (95% CI) 0.66 (0.51-0.86), p=0.002] after initiation of dialysis. Results were similar in all subgroups of patients stratified by age, race, type of intervention, presence/absence of myocardial infarction, congestive heart failure and diabetes. Conclusion: CABG in advanced CKD patients was associated lower risk of death after initiation of dialysis compared to PCI.

Acute kidney injury following coronary revascularization procedures in patients with advanced CKD.

BACKGROUND: Previous studies reported that compared with percutaneous coronary interventions (PCIs), coronary artery bypass grafting (CABG) is associated with a reduced risk of mortality and repeat revascularization in patients with mild to moderate chronic kidney disease (CKD) and end-stage renal disease (ESRD). Information about outcomes associated with CABG versus PCI in patients with advanced stages of CKD is limited. We evaluated the incidence and relative risk of acute kidney injury (AKI) associated with CABG versus PCI in patients with advanced CKD. METHODS: We examined 730 US veterans with incident ESRD who underwent a first CABG or PCI up to 5 years prior to dialysis initiation. The association of CABG versus PCI with AKI was examined in multivariable adjusted logistic regression analyses. RESULTS: A total of 466 patients underwent CABG and 264 patients underwent PCI. The mean age was 64 ± 8 years, 99% were male, 20% were African American and 84% were diabetic. The incidence of AKI in the CABG versus PCI group was 67% versus 31%, respectively (P < 0.001). The incidence of all stages of AKI were higher after CABG compared with PCI. CABG was associated with a 4.5-fold higher crude risk of AKI {odds ratio [OR] 4.53 [95% confidence interval (CI) 3.28-6.27]; P < 0.001}, which remained significant after multivariable adjustments [OR 3.50 (95% CI 2.03-6.02); P < 0.001]. CONCLUSION: CABG was associated with a 4.5-fold higher risk of AKI compared with PCI in patients with advanced CKD. Despite other benefits of CABG over PCI, the extremely high risk of AKI associated with CABG should be considered in this vulnerable population when deciding on the optimal revascularization strategy.

Disparities in Population-Level Socio-Economic Factors Are Associated with Disparities in Preoperative Clinical Risk Factors in Children.

African American children are more than twice as likely as white American children to die after surgery, and have increased risk for longer hospital stays, post-surgical complications, and higher hospital costs. Prior research into disparities in pediatric surgery outcomes has not considered interactions between patient-level Clinical Risk Factors (CRFs) and population-level Social, Economic, and Environmental Factors (SEEFs) primarily due to the lack of integrated data sets. In this study, we analyze correlations between SEEFs and CRFs and correlations between CRFs and surgery outcomes. We used a dataset from a cohort of 460 surgical cases who underwent surgery at a children's hospital in Memphis, Tennessee in the United States. The analysis was conducted on 23 CRFs, 9 surgery outcomes, and 10 SEEFs and demographic variables. Our results show that population-level SEEFs are significantly associated with both patient-level CRFs and surgery outcomes. These findings may be important in the improved understanding of health disparities in pediatric surgery outcomes.

Race, Preoperative Risk Factors, and Death After Surgery.

BACKGROUND AND OBJECTIVES: African American children are more than twice as likely to die after surgery compared with white children. In this study, we evaluated whether risk factors for death after surgery differ for African American and white children, and we also assessed whether race-specific risk stratification models perform better than non-race-specific models. METHODS: The National Surgical Quality Improvement Program Pediatric Participant Use Data File contains clinical data on operations performed on children at participating institutions in the United States. Variables predictive of death within 30 days of surgery were analyzed for differences in prevalence and strength of association with death for both African American and white children. Classification tree and network analysis were used. RESULTS: Network analyses revealed that the prevalence of preoperative risk factors associated with death after surgery was significantly higher for African American than for white children. In addition, many of the risk factors associated with death after surgery carried a higher risk when they occurred in African American children. Race-specific risk models provided high accuracy, with a specificity of 94% and a sensitivity of 83% for African American children and a specificity of 96% and a sensitivity of 77% for white children, and yet these 2 models were significantly different from each other. CONCLUSIONS: Race-specific models predict outcomes after surgery more accurately compared with non-race-specific models. Identification of race-specific modifiable risk factors may help reduce racial disparities in surgery outcome.

A novel risk classification system for 30-day mortality in children undergoing surgery.

A simple, objective and accurate way of grouping children undergoing surgery into clinically relevant risk groups is needed. The purpose of this study, is to develop and validate a preoperative risk classification system for postsurgical 30-day mortality for children undergoing a wide variety of operations. The National Surgical Quality Improvement Project-Pediatric participant use file data for calendar years 2012-2014 was analyzed to determine preoperative variables most associated with death within 30 days of operation (D30). Risk groups were created using classification tree analysis based on these preoperative variables. The resulting risk groups were validated using 2015 data, and applied to neonates and higher risk CPT codes to determine validity in high-risk subpopulations. A five-level risk classification was found to be most accurate. The preoperative need for ventilation, oxygen support, inotropic support, sepsis, the need for emergent surgery and a do not resuscitate order defined non-overlapping groups with observed rates of D30 that vary from 0.075% (Very Low Risk) to 38.6% (Very High Risk). When CPT codes where death was never observed are eliminated or when the system is applied to neonates, the groupings remained predictive of death in an ordinal manner.

The impact of a simulation-based training lab on outcomes of hysterectomy.

OBJECTIVE: To evaluate the impact of a simulation-based training lab on surgical outcomes of different hysterectomy approaches in a resident teaching tertiary care center. MATERIAL AND METHODS: This retrospective cohort study was conducted at The University of Texas, Department of Obstetrics and Gynecology. In total, 1397 patients who had undergone total abdominal hysterectomy (TAH), vaginal hysterectomy (VH), total laparoscopy-assisted hysterectomy (TLH), or robot-assisted hysterectomy (RAH) for benign gynecologic conditions between 2009 and 2014 were included in the study. The comparison was made according to the year when the surgeries were performed: 2009 (before simulation training) and the combination of 2010-2014 (after simulation training) for each technique (TAH, VH, and LAH). Since a simulation lab for robotic surgery was introduced in 2010 at our institute, the comparison for robotic surgery was made between the combination of 2009-2010 as the control and the combination of 2010-2014 as the study group. RESULTS: The average estimated blood loss before and after simulation-based training was significantly different in TAH and RAH groups (317±170 mL versus 257±146 mL, p=0.003 and 154±107 mL versus 102±88 mL, p=0.004, respectively), but no difference was found for TLH and VH. The mean of length of hospital stay was significantly different before and after simulation-based training for each technique: 3.7±2.3 versus 2.9±2.2 days for TAH, 2.0±1.2 versus 1.3±0.9 days for VH, 2.4±1.3 versus 1.9±2.5 days for TLH, and 2.0±1.3 versus 1.4±1.7 days for RAH (p<0.01). CONCLUSION: Based on our data, simulator-based training may play an integrative role in developing the residents' surgical skills and thus improving the surgical outcomes of hysterectomy.

Natal cleft deeper in patients with pilonidal sinus: implications for choice of surgical procedure.

PURPOSE: The study was planned to evaluate the depth of natal cleft in patients with pilonidal sinus disease and in healthy persons. METHODS: The study included 50 patients with pilonidal sinus disease and 51 volunteers. Data including body mass index and natal cleft depth were recorded. Natal cleft depth was measured in millimeters by using a caliper instrument. Data were evaluated with the use of the statistical package program (SPSS) with a chi-squared test analysis. P < 0.01 was evaluated as significant. RESULTS: There was no discernable difference in age, occupation, and sex between the groups. The mean natal cleft depth was 27.06 mm in the pilonidal sinus group and 21.07 in the nonpilonidal sinus group. The differences between the two groups were statistically significant (P < 0.01) for natal cleft depth. The mean body mass index was 25.71 in the pilonidal sinus group and 25.28 in the nonpilonidal sinus group. The difference between groups was statistically insignificant for body mass index. CONCLUSIONS: The natal cleft of patients with pilonidal sinus disease is deeper than the natal cleft of members of the volunteer group.