Rate of Change in Cardiac Magnetic Resonance Imaging Measures Is Associated With Death in Duchenne Muscular Dystrophy.

BACKGROUND: Cardiovascular disease is the leading cause of death among patients with Duchenne muscular dystrophy (DMD). Identifying patients at risk of early death could allow for increased monitoring and more intensive therapy. Measures that associate with death could serve as surrogate outcomes in clinical trials. METHODS AND RESULTS: Duchenne muscular dystrophy subjects prospectively enrolled in observational studies were included. Models using generalized least squares were used to assess the difference of cardiac magnetic resonance measurements between deceased and alive subjects. A total of 63 participants underwent multiple cardiac magnetic resonance imaging and were included in the analyses. Twelve subjects (19.1%) died over a median follow-up of 5 years (interquartile range, 3.1-7.0). Rate of decline in left ventricular ejection fraction was faster in deceased than alive subjects (P<0.0001). Rate of increase in indexed left ventricular end-diastolic (P=0.0132) and systolic (P<0.0001) volumes were higher in deceased subjects. Faster worsening in midcircumferential strain was seen in deceased subjects (P=0.049) while no difference in global circumferential strain was seen. The rate of increase in late gadolinium enhancement, base T1, and mid T1 did not differ between groups. CONCLUSIONS: Duchenne muscular dystrophy death is associated with the rate of change in left ventricular ejection fraction, midcircumferential strain, and ventricular volumes. Aggressive medical therapy to decrease the rate of progression may improve the mortality rate in this population. A decrease in the rate of progression may serve as a valid surrogate outcome for therapeutic trials.

Clinical prediction model: Multisystem inflammatory syndrome in children versus Kawasaki disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD). OBJECTIVE: The study objective was to develop a prediction model to assist with this diagnostic dilemma. METHODS: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients. RESULTS: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99). CONCLUSIONS: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.

Incidence and types of congenital heart disease at a referral hospital in Jordan: retrospective study from a tertiary center.

Background: Congenital heart disease (CHD) is the most common birth defect and accounts for significant global morbidity and mortality. Relatively little is known about the epidemiology of CHD in Jordan or the manner in which CHD is identified. Methods: A retrospective medical record review was conducted for all neonates who had an abnormal echocardiogram performed at a tertiary referral hospital. All included neonates had echocardiography performed by the same pediatric cardiologist at the discretion of the treatment team. Descriptive statistics were used to describe CHD incidence, types of CHD identified, and mechanism of identification. Results: The incidence of congenital heart disease was 17.8 per 1,000 live births. This rose to 24.6 per 1,000 if patent ductus arteriosus in preterm infants was included. The most common identified abnormalities were PDA, atrial septal defects, persistent pulmonary hypertension, septal hypertrophy, and ventricular septal defects. Most children were evaluated either for a murmur heard on exam or as a part of screening due to other comorbidities or risk factors. Less than 1% of children had a prenatal diagnosis. There was a higher rate of persistent pulmonary hypertension during the COVID-19 pandemic than before (p < 0.001). Conclusions: There is a high incidence of CHD in Jordan. Increased prenatal and perinatal screening for CHD may allow for earlier detection.

Prevalence and determinants of contraception utilization over time in Migori County, Kenya: Repeated cross-sectional household surveys.

Contraception use and family planning have been shown to save lives and benefit women, their families, and their communities. We conducted a cross-sectional study analyzing data from a 2021 survey that was conducted across eight different regions in Migori County, Kenya to examine the potential role that different factors play in meeting family planning targets. Comparisons are made to data collected in 2018/2019 in order to estimate the change over time of contraception uptake. Descriptive statistics were calculated, the Cochran-Mantel-Haenszel test was used to compare contraception use over time, and multivariable logistic regression was used to model determinants of contraceptive use. Sixty-four percent of respondents in 2021 reported that they currently use some form of contraception, and implants are the most popular contraceptive method. Factors associated with higher contraception usage were region, ages 25-34 years, and marital status. Contraception uptake increased significantly in East Kamagambo following a community-driven sexual and reproductive health intervention by the Lwala Community Alliance, suggesting that increased investment in family planning may be influential. We recommend targeted outreach to population groups with low uptake of contraception and investment in both demand- and supply-side interventions to increase contraceptive uptake. Additional research, especially for populations under 18, is needed to further inform effective investment and policy.

Childhood mortality and associated factors in Migori County, Kenya: evidence from a cross-sectional survey.

OBJECTIVES: The under-five mortality (U5M) rate in Kenya (41 per 1000 live births) remains significantly above international goals (25 per 1000 live births). This is further exacerbated by regional inequalities in mortality. We aimed to describe U5M in Migori County, Kenya, and identify associated factors that can serve as programming targets. DESIGN: Cross-sectional observational survey. SETTING: Areas served by the Lwala Community Alliance and control areas in Migori County, Kenya. PARTICIPANTS: This study included 15 199 children born to respondents during the 18 years preceding the survey. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was mortality in the first 5 years of life. The survey was powered to detect a 10% change in various health metrics over time with 80% power. RESULTS: A total of 15 199 children were included in the primary analyses, and 230 (1.5%) were deceased before the fifth birthday. The U5M rate from 2016 to 2021 was 32.2 per 1000 live births. Factors associated with U5M included year of birth (HR 0.926, p<0.001), female sex (HR 0.702, p=0.01), parental marriage (HR 0.642, p=0.036), multiple gestation pregnancy (HR 2.776, p<0.001), birth spacing less than 18 months (HR 1.894, p=0.005), indoor smoke exposure (HR 1.916, p=0.027) and previous familial contribution to the National Hospital Insurance Fund (HR 0.553, p=0.009). The most common cause of death was malaria. CONCLUSIONS: We describe factors associated with childhood mortality in a Kenyan community using survival analyses of complete birth histories. Mortality rates will serve as the baseline for future programme evaluation as a part of a 10-year study design. This provides both the hyperlocal information needed to improve programming and generalisable conclusions for other organisations working in similar environments.

Training and experience outperform literacy and formal education as predictors of community health worker knowledge and performance, results from Rongo sub-county, Kenya.

Introduction: There is a growing recognition that Community Health Workers are effective at improving health outcomes and expanding health access. However, the design elements that lead to high-quality Community Health Worker programing are relatively understudied. We looked at the predictors of Community Health Worker knowledge of obstetric and early infant danger signs as well as performance in achieving antenatal care and immunization uptake among their clients. Methods: The study takes place in the context of an intervention implemented jointly by Lwala Community Alliance and the Kenya Ministry of Health which sought to professionalize Community Health Worker cadres through enhanced training, payment, and supervision. There were four cohorts included in the study. Two cohorts started receiving the intervention prior to the baseline, one cohort received the intervention between the baseline and endline, and a final cohort did not receive the intervention. Data on Community Health Worker demographics, knowledge tests, and key performance indicators were collected for 234 Community Health Workers. Regression analyses were used to explore education, literacy, experience, training, and gender as potential predictors of CHW performance. Results: We found that clients of Community Health Workers trained through the intervention were 15% more likely to be fully immunized and 14% more likely to have completed four or more antenatal care visits. Additionally, recency of training and experience caring for pregnant women were associated with increased Community Health Worker knowledge. Finally, we found no association between gender and CHW competency and tenuous associations between education/literacy and Community Health Worker competency. Discussion: We conclude that the intervention was predictive of increased Community Health Worker performance and that recency of training and experience were predictive of increased knowledge. Though education and literacy are often used in the selection processes of Community Health Workers globally, the link between these characteristics and Community Health Worker knowledge and performance are mixed. Thus, we encourage further research into the predictive value of common Community Health Worker screening and selection tools. Further, we encourage policymakers and practitioners to reconsider the use of education and literacy as means of Community Health Worker selection.

Novel Cardiac Imaging Risk Score for Mortality Prediction in Duchenne Muscular Dystrophy.

Cardiovascular disease is the leading cause of death in patients with Duchenne Muscular Dystrophy (DMD), but there is significant cardiomyopathy phenotypic variability. Some patients demonstrate rapidly progressive disease and die at a young age while others survive into the fourth decade. Criteria to identify DMD subjects at greatest risk for early mortality could allow for increased monitoring and more intensive therapy. A risk score was created describing the onset and progression of left ventricular dysfunction and late gadolinium enhancement in subjects with DMD. DMD subjects prospectively enrolled in ongoing observational studies (which included cardiac magnetic resonance [CMR]) were used to validate the risk score. A total of 69 subjects had calculable scores. During the study period, 12 (17%) died from complications of DMD. The median risk score was 3 (IQR [2,5]; range [0,9]). The overall risk score applied at the most recent imaging age was associated with mortality at a median age of 17 years (IQR [16,20]) (HR 2.028, p < 0.001). There were no deaths in subjects with a score of less than two. Scores were stable over time. An imaging-based risk score allows risk stratification of subjects with DMD. This can be quickly calculated during a clinic visit to identify subjects at greatest risk of early death.

Feasibility of focused cardiac ultrasound training for non-cardiologists in a resource-limited setting using a handheld ultrasound machine.

BACKGROUND: Heart disease remains a leading cause of morbidity and mortality, particularly in low- and middle-income countries. Access to diagnostic modalities is limited in these settings. Limited echocardiographic studies performed by non-cardiologists can increase access, improve diagnosis and allow for earlier medical therapy. METHODS: Two internal medicine residents at a tertiary-level hospital in Ghana were trained to perform limited echocardiographic studies. Each trainee performed 50 echocardiograms and interpreted 20 studies across three predetermined timepoints. Interpretation was compared to expert interpretation. RESULTS: Agreement improved over time. At the final evaluation, there was high agreement across all aspects: left ventricular structure (70%, kappa 0.52, p = 0.01), left ventricular function (80%, kappa 0.65, p = 0.004), right ventricular structure (90%, kappa 0.71, p = 0.002), right ventricular function (100%, kappa 1.00, p < 0.001), and presence of effusion (100%, kappa 1.00, p < 0.001). CONCLUSIONS: Non-cardiologists can be trained in focused echocardiography using handheld machines. Such training can increase access to diagnostic capabilities in resource-limited settings.

Characterizing multidimensional poverty in Migori County, Kenya and its association with depression.

INTRODUCTION: Narrow, unidimensional measures of poverty often fail to measure true poverty and inadequately capture its drivers. Multidimensional indices of poverty more accurately capture the diversity of poverty. There is little research regarding the association between multidimensional poverty and depression. METHODS: A cross-sectional survey was administered in five sub-locations in Migori County, Kenya. A total of 4,765 heads of household were surveyed. Multidimensional poverty indices were used to determine the association of poverty with depression using the Patient Health Questionnaire (PHQ-8) depression screening tool. RESULTS: Across the geographic areas surveyed, the overall prevalence of household poverty (deprivation headcount) was 19.4%, ranging from a low of 13.6% in Central Kamagambo to a high of 24.6% in North Kamagambo. Overall multidimensional poverty index varied from 0.053 in Central Kamagambo to 0.098 in North Kamagambo. Of the 3,939 participants with depression data available, 481 (12.2%) met the criteria for depression based on a PHQ-8 depression score ≥10. Poverty showed a dose-response association with depression. CONCLUSIONS: Multidimensional poverty indices can be used to accurately capture poverty in rural Kenya and to characterize differences in poverty across areas. There is a clear association between multidimensional poverty and depressive symptoms, including a dose effect with increasing poverty intensity. This supports the importance of multifaceted poverty policies and interventions to improve wellbeing and reduce depression.

Population-based socio-demographic household assessment of livelihoods and health among communities in Migori County, Kenya over multiple timepoints (2021, 2024, 2027): A study protocol.

Migori County is located in western Kenya bordering Lake Victoria and has traditionally performed poorly on important health metrics, including child mortality and HIV prevalence. The Lwala Community Alliance is a non-governmental organization that serves to promote the health and well-being of communities in Migori County through an innovative model utilizing community health workers, community committees, and high-quality facility-based care. This has led to improved outcomes in areas served, including improvements in childhood mortality. As the Lwala Community Alliance expands to new programming areas, it has partnered with multiple academic institutions to rigorously evaluate outcomes. We describe a repeated cross-sectional survey study to evaluate key health metrics in both areas served by the Lwala Community Alliance and comparison areas. This will allow for longitudinal evaluation of changes in metrics over time. Surveys will be administered by trained enumerators on a tablet-based platform to maintain high data quality.

Determinants of modern contraceptive prevalence and unplanned pregnancies in Migori County, Kenya: results of a cross-sectional household survey.

Family planning and contraceptive utilization can have significant effects in reducing unplanned pregnancies and improving maternal and child health indicators. We conducted a retrospective cross-sectional study analyzing survey response data within six sub-locations of Migori County, Kenya in 2018 and 2019. We utilize this survey data to estimate both the prevalence of contraceptive uptake and unwanted pregnancies in the study populations, and to examine the potential role that different factors play in meeting related family planning targets. Descriptive statistics were calculated, and multivariable logistic regression was used to model determinants of contraceptive use and reported unplanned pregnancy. A total of 3,642 female heads of household were included. 63% of respondents reported that they currently use some form of contraception, and the prevalence of unplanned pregnancy was 36.7%. Our findings reflect the need for family planning programs to focus interventions on those at highest risk. There is a need for additional research and investigation into community and individual beliefs surrounding family planning in order to ensure that interventions are culturally sensitive and locally responsive.

Lassa Fever among Children in Eastern Province, Sierra Leone: A 7-year Retrospective Analysis (2012-2018).

Pediatric Lassa fever (LF) usually presents as a nonspecific febrile illness, similar to other endemic diseases in countries like Sierra Leone, where LF is considered to be hyperendemic. The nonspecificity of presentation and lack of research have made it difficult to fully understand best practices for pediatric management. We aim to describe clinical characteristics of hospitalized pediatric patients suspected or diagnosed with LF and assess factors associated with hospital outcomes among those with LF antigen-positive results. We conducted a 7-year retrospective cohort study using routine data for all children younger than 18 years admitted at the Kenema Government Hospital's LF ward. A total of 292 children with suspected or confirmed LF were analyzed. Overall, mortality was high (21%). Children with antigen-positive results had a high case fatality rate of 63% (P < 0.01). In univariate analyses, children who presented with unexplained bleeding (odds ratio [OR]: 3.58; 95% CI: 1.08-11.86; P = 0.040) and confusion (altered sensorium) (OR: 5.37; 95% CI: 1.34-21.48; P = 0.020) had increased odds of death. Abnormal serum levels of alanine aminotransferase (P = 0.001), creatinine (P = 0.004), and potassium (P = 0.003) were associated with increased likelihood of death in these children. Treatment with ribavirin was not significantly associated with survival (P = 0.916). Our findings provide insights into current pediatric LF clinical presentation and management. More evidence-based, high-quality research in creating predictive algorithms of antigen-positivity and hospital outcomes is needed in the management of pediatric LF.

Predicting No-shows at a Student-Run Comprehensive Primary Care Clinic.

BACKGROUND AND OBJECTIVES: Missed appointments represent a significant challenge to the efficient and effective provision of care in the outpatient setting. High no-show rates result in ineffective use of human resources and contribute to loss of follow-up. Shade Tree Clinic (STC) is a student-run, comprehensive primary care clinic that serves more than 350 Middle Tennessee residents. This study aimed to use available data to predict no-shows to improve clinic efficiency and service quality. METHODS: Data were pulled from clinic scheduling software for all appointments at STC between January 1, 2010 and December 31, 2015. Weather data were added for each appointment date using an online database. Multivariable logistic regression was used to create models from these historical data. RESULTS: A total of 13,499 appointments were included with an overall show rate of 69.2%. The final model contained previous show rate (OR 1.063; P<.001), day of the week (OR 1.20; P<.001), automated reminder (OR 1.40; P<.001), snow in inches (OR .33; P<.001), and high ambient temperature in degrees (OR 1.01; P<.001). Using a cutoff probability of the 25th percentile, the model had a negative predictive value of 61.0%. CONCLUSIONS: Based on readily available data and a novel conceptual framework, we can identify the quarter of patients least likely to present for scheduled appointments and target them for interventions, allowing care providers to more effectively address community health care disparities through the clinic. This analysis is replicable at any clinic using an electronic medical record.

Safe Anesthesia Care in Western Kenya: A Preliminary Assessment of the Impact of Nurse Anesthetists at Multiple Levels of Government Hospitals.

BACKGROUND: Only 20% of the surgical burden in eastern sub-Saharan Africa is currently met, leaving >17 million surgical cases annually in need of safe surgery and anesthesia. Similarly, there is an extreme shortage of anesthesia providers in East Africa, with just 0.44 anesthesiologists per 100,000 people in Kenya compared to 20.82 per 100,000 in the United States. Additionally, surgical access is not equally distributed within countries, with rural settings often having the greatest unmet need. We developed and tested a set of tools to assess if graduates of the Kenya registered nurse anesthetist (KRNA) training program, who were placed in rural hospitals in Kenya, would have any impact on surgical numbers, referral patterns, and economics of these hospitals. METHODS: Cross-sectional data were collected from facility assessments in 9 referral hospitals to evaluate the possible impact of the KRNAs on anesthesia care. The hospitals were grouped based on both the number of beds and the assigned national hospital level. At each level, a hospital that had KRNA graduates (intervention) was matched with comparison hospitals in the same category with no KRNA graduates (control). The facility assessment survey included questions capturing data on personnel, infrastructure, supplies, medications, procedures, and outcomes. At the intervention sites, the medical directors of the hospitals and the KRNAs were interviewed. Descriptive statistics were used to present the findings. RESULTS: Intervention sites had a density of anesthesia providers that was 43% higher compared to the control sites. Intervention sites performed at least twice as many surgical cases compared to the control sites. Most KRNAs stated that the anesthesia training program had given them sufficient training and leadership skills to perform safe anesthesia in their clinical practice setting. Medical directors at the intervention sites reported increased surgical volumes and fewer referrals to larger hospitals due to the anesthesia gaps that had been addressed. CONCLUSIONS: Our findings from this study suggest that KRNAs may be associated with an increased volume of surgical cases completed in these rural Kenyan hospitals and may therefore be filling a known anesthetic void. The presence of skilled anesthesia providers is a first step toward providing safe surgery and anesthesia care for all; however, significant gaps still remain. Future analysis will focus on surgical outcomes, the appropriate anesthesia delivery model for a rural population, and how the availability of anesthesia infrastructure impacts referral patterns and safe surgery capacity.

Under-five mortality in the Rongo Sub-County of Migori County, Kenya: Experience of the Lwala Community Alliance 2007-2017 with evidence from a cross-sectional survey.

INTRODUCTION: Childhood mortality remains a pressing problem in rural Kenya, and reducing under-five deaths is a key target of the Sustainable Development Goals. We aim to describe the reduction in under-five mortality in a rural Kenyan community served by the Lwala Community Alliance and factors associated with under-five mortality in this community. METHODS: A cross-sectional survey containing a complete birth history was administered to a representative sample of the catchment area of the Lwala Community Alliance. Survival analysis techniques were used to describe temporal trends and risk factors related to under-five mortality. RESULTS: 1,362 children were included in the study, and 91 children died before the fifth birthday. The most common causes of death among children under five were malaria (19%), respiratory infection (13%), and anemia (11%). The under-five mortality rate was 104.8 per 1,000 live births from 1999 to 2006 and 53.0 per 1,000 after the founding of the Lwala Community Alliance in 2007. Factors associated with under-five mortality included year of birth (HR 0.931; 95% CI: 0.877, 0.988; p = 0.019), multiple-gestation pregnancy (HR 6.201; 95% CI: 2.073, 18.555; p < 0.001), and birth in the long rain season (HR 1.981; 95% CI: 1.350, 2.907; p < 0.001). Birth spacing greater than 18 months was negatively associated with under-five mortality (HR 0.345; 95% CI: 0.203, 0.587; p < 0.001). CONCLUSIONS: There was a significant decrease in under-five mortality before and after the presence of the Lwala Community Alliance. Multiple-gestation pregnancies, birth season, and short birth spacing were associated with under-five mortality and provide possible targets to further reduce mortality in the region. This provides both hyper-local data necessary for implementation efforts and generalizable data and sampling methods that may be useful for other implementing organizations in sub-Saharan Africa.

Characterization of pediatric golf cart injuries to guide injury prevention efforts.

BACKGROUND: Golf cart injuries represent an increasing source of morbidity and mortality in the United States. Characterization of the circumstances of these injuries can inform injury prevention efforts. METHODS: This study retrospectively reviews a prospective trauma registry at a level-one pediatric trauma center for golf cart-related injuries in patients under 18years of age admitted to the hospital between 2008 and 2016. RESULTS: The 40 identified crashes were associated with 82 hospital days, 17 ICU days, and more than $1 million in hospital charges over the study period. The median hospital stay was 1.5days, and the median hospital charge was $20,489. Severe injuries with an Injury Severity Score of >15 were identified in 25% of patients, and moderate injuries with scores between nine and 15 were identified in an additional 30%. The most common injures were head and neck (60%) and external injuries to the body surface (52.5%). Only a single child was wearing a seatbelt, and the vast majority was not using any safety equipment. Children as young as nine years old were driving golf carts, and child drivers were associated with the cart overturning (p=0.007). CONCLUSIONS: Golf cart crashes were a source of substantial morbidity at a level-one trauma center. Increased safety measures, such as higher hip restraints, seatbelts, and front-wheel breaks could substantially increase the safety of golf carts. Increased regulation of driving age as well as driver education may also reduce these injuries.

Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network.

OBJECTIVES: The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN). METHODS: Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both "traditional" multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters. RESULTS: Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.

Automated Case Cancellation Review System Improves Systems-Based Practice.

BACKGROUND: Accreditation Council for Graduate Medical Education (ACGME) core competencies of systems-based practice and practice-based learning and improvement are difficult to assess, as they are often not directly measurable or observable. Reviewing day-of-surgery cancellations could provide resident learning opportunities in these areas. OBJECTIVE: An automated system to facilitate anesthesiology resident review of cancelled cases was implemented on the Preoperative Evaluation Clinic (PEC) rotation at the authors' institution. This study aims to evaluate its impact on resident education. METHODS: Residents on the PEC rotation during the 6 months preceding (n = 22) and following (n = 13) implementation in 2014 were surveyed about their experience performing cancelled case reviews in order to ascertain the effect of the intervention on their training. RESULTS: Significant changes were reported in the number of cases reviewed by each resident (p < 0.0001), perceived importance of review (p = 0.03), and ease of review (p = 0.03) after system implementation. There was also an increase in the proportion of cancelled cases reviewed from 17.3% (34 of 196) to 95.6% (194 of 203) (p < 0.0001). Non-significant trends were seen in perceived rotation effect on ACGME competencies, including systems-based practice. Several specific improvements to our clinical practice, including the creation of standardized guidelines, arose from these case reviews. CONCLUSION: Implementation of automated systems can improve compliance with educational goals by clarifying priorities and simplifying workflow. This system increased the number of cases reviewed by residents and the perceived importance of this review as a part of their educational experience.

Metadata from data: identifying holidays from anesthesia data.

The increasingly large databases available to researchers necessitate high-quality metadata that is not always available. We describe a method for generating this metadata independently. Cluster analysis and expectation-maximization were used to separate days into holidays/weekends and regular workdays using anesthesia data from Vanderbilt University Medical Center from 2004 to 2014. This classification was then used to describe differences between the two sets of days over time. We evaluated 3802 days and correctly categorized 3797 based on anesthesia case time (representing an error rate of 0.13%). Use of other metrics for categorization, such as billed anesthesia hours and number of anesthesia cases per day, led to similar results. Analysis of the two categories showed that surgical volume increased more quickly with time for non-holidays than holidays (p < 0.001). We were able to successfully generate metadata from data by distinguishing holidays based on anesthesia data. This data can then be used for economic analysis and scheduling purposes. It is possible that the method can be expanded to similar bimodal and multimodal variables.