Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis.

BACKGROUND: Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM. METHODS: A total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost. RESULTS: NFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79). CONCLUSION: The good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status. IMPACT: Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.

Congenital anomalies and predisposition to severe COVID-19 among pediatric patients in the United States.

BACKGROUND AND OBJECTIVE: Congenital heart defects are known to be associated with increased odds of severe COVID-19. Congenital anomalies affecting other body systems may also be associated with poor outcomes. This study is an exhaustive assessment of congenital anomalies and odds of severe COVID-19 in pediatric patients. METHODS: Data were retrieved from the COVID-19 dataset of Cerner® Real-World Data for encounters from March 2020 to February 2022. Prior to matching, the data consisted of 664,523 patients less than 18 years old and 927,805 corresponding encounters with COVID-19 from 117 health systems across the United States. One-to-one propensity score matching was performed, and a cumulative link mixed-effects model with random intercepts for health system and patients was built to assess corresponding associations. RESULTS: All congenital anomalies were associated with worse COVID-19 outcomes, with the strongest association observed for cardiovascular anomalies (odds ratio [OR], 3.84; 95% CI, 3.63-4.06) and the weakest association observed for anomalies affecting the eye/ear/face/neck (OR, 1.16; 95% CI, 1.03-1.31). CONCLUSIONS AND RELEVANCE: Congenital anomalies are associated with greater odds of experiencing severe symptoms of COVID-19. In addition to congenital heart defects, all other birth defects may increase the odds for more severe COVID-19. IMPACT: All congenital anomalies are associated with increased odds of severe COVID-19. This study is the largest and among the first to investigate birth defects across all body systems. The multicenter large data and analysis demonstrate the increased odds of severe COVID19 in pediatric patients with congenital anomalies affecting any body system. These data demonstrate that all children with birth defects are at increased odds of more severe COVID-19, not only those with heart defects. This should be taken into consideration when optimizing prevention and intervention resources within a hospital.

Pediatric Asthma Exacerbations: 14-Day Emergency Department Return Visit Risk Factors.

BACKGROUND: Asthma, the most common chronic disease of childhood, can affect a child's physical and mental health and social and emotional development. OBJECTIVE: The aim of this study was to identify factors associated with emergency department (ED) return visits for asthma exacerbations within 14 days of an initial visit. METHODS: This was a retrospective review from Cerner Real-World Data for patients aged from 5 to 18 years and seen at an ED for an asthma exacerbation and discharged home at the index ED visit. Asthma visits were defined as encounters in which a patient was diagnosed with asthma and a beta agonist, anticholinergic, or systemic steroid was ordered or prescribed at that encounter. Return visits were ED visits for asthma within 14 days of an index ED visit. Data, including demographic characteristics, ED evaluation and treatment, health care utilization, and medical history, were collected. Data were analyzed via logistic regression mixed effects model. RESULTS: A total of 80,434 index visits and 17,443 return visits met inclusion criteria. Prior ED return visits in the past year were associated with increased odds of a return visit (odds ratio [OR] 2.12; 95% CI 2.07-2.16). History of pneumonia, a concomitant diagnosis of pneumonia, and fever were associated with increased odds of a return visit (OR 1.19; 95% CI 1.10-1.29; OR 1.15; 95% CI 1.04-1.28; OR 1.20; 95% CI 1.11-1.30, respectively). CONCLUSIONS: Several variables seem to be associated with statistically significant increased odds of ED return visits. These findings indicate a potentially identifiable population of at-risk patients who may benefit from additional evaluation, planning, or education prior to discharge.

Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission.

OBJECTIVE: This study aimed to assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU). STUDY DESIGN: This is a retrospective study that uses the study site's Children's Hospitals Neonatal Database and electronic health records. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and birth weight was tested for statistical significance. RESULTS: A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission among low birthweight premature patients increase with increasing gestational age, whereas decrease with increasing gestational age among their normal or high birth weight peers. CONCLUSION: The effect of gestational age on odds of hospital readmission is dependent on birth weight. KEY POINTS: · Population included patients discharged from a NICU between January 2017 and March 2020.. · A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge.. · The effect of gestational age on odds of hospital readmission is dependent on birth weight..

A pilot study to improve provider adherence to NAEPP guidelines.

The prevalence and morbidity of Asthma in the United States has increased since the 1991 National Asthma Education and Prevention Program (NAEPP) and updated Expert Panel Report -3 (EPR-3) guidelines in 2007 were published. To improve provider adherence to the NAEPP EPR-3 guidelines Children's Hospital of Orange County (CHOC) in California integrated the HealtheIntentSM Pediatric Asthma Registry (PAR) into the electronic medical record (EMR) in 2015. METHODS: A serial cross-sectional design was used to compare provider management of CHOC MediCal asthma patients before 2014 (N = 6606) and after 2018 (N = 6945) integration of the Registry with NAEPP guidelines into the EMR. Four provider adherence measures (Asthma Control Test [ACT], Asthma Action Plan [AAP], inhaled corticosteroids [ICS] and spacers) were evaluated using General Linear Mixed Models and Chi square. FINDINGS: In 2018, patients were more likely to receive an ACT, (OR = 14.95, 95% CI 12.67, 17.65, p < .001), AAP (OR = 12.70, 95% CI 11.10, 14.54, p < .001), ICS (OR = 1.85, 95% CI 8.52, 14.54, p < .001) and spacer (OR = 1.45, 95% CI 1.31, 1.6, p < .001) compared to those in 2014. DISCUSSION: The pilot study showed integration of the Pediatric Asthma Registry into the EMR, as a computer decision support tool that was an effective intervention to increase provider adherence to NAEPP guidelines. Ongoing monitoring and education are needed to promote and sustain provider behavioral change. Additional research to include multi-sites and decreased time between evaluation years is recommended. APPLICATION TO PRACTICE: Can be used for excellent health policy decision making as a direct impact on patient care and outcomes, by improving provider adherence to the NAEPP guidelines.

Neighborhood disadvantage and pediatric inpatient opioid prescription patterns.

BACKGROUND: To explore the role of children's residential environment on opioid prescribing patterns in a predominantly Latinx sample. METHODS: We connected geocoded data from electronic medical records in a diverse sample of pediatric patients to neighborhood environments constructed using latent profile modeling techniques. We then estimated a series of multilevel models to determine whether opioid prescribing patterns vary by residential context. RESULTS: A stepwise pattern exists between neighborhood disadvantage and pediatric opioid prescription patterns, such that higher levels of disadvantage associate with a greater likelihood of opioid prescription, independent of the patient's individual profile. CONCLUSION: In a largely Latinx sample of children, the neighborhood in which a child lives influences whether or not they will receive opioids. Considering the differences in patient residential environment may reduce variation in opioid dispensing rates among pediatric patients.

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. METHODS: In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients' discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). RESULTS: The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. CONCLUSIONS: For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

Multivariable Analysis of Patient Satisfaction in the Pediatric Emergency Department.

BACKGROUND: Published data on predictive factors associated with parent satisfaction from care in a pediatric emergency department (ED) visit are limited to be descriptive and obtained from small data sets. Accordingly, the purpose of this study was to determine both modifiable and nonmodifiable demographic and operational factors that influence parental satisfaction using a large and ethnically diverse site data set. METHODS: Data consist of responses to the National Research Council (NRC) survey questionnaires and electronic medical records of 15,895 pediatric patients seen in a pediatric ED between the ages of 0 and 17 years discharged from May 2018 to September 2019. Bivariate, χ2, and multivariable logistic regression analyses were carried out using the NRC item on rating the ED between 0 and 10 as the primary outcome. Responses were coded using a top-box approach, a response of "9" or "10" represented satisfaction with the facility, and every other response was indicated as undesirable. Demographic data and NRC questionnaire were used as potential predictors. RESULTS: Multivariable regression analysis found the following variables as independent predictors for positive parental rating of the ED: Hispanic race/ethnicity (odds ratio [OR], 1.285), primary language Spanish (OR, 2.399), and patients who had government-sponsored insurance (OR, 1.470). Those survey items with the largest effect size were timeliness of care (OR, 0.188) and managing discomfort (OR, 0.412). CONCLUSIONS: Parental rating of an ED is associated with nonmodifiable variables such as ethnicity and modifiable variables such as timeliness of care and managing discomfort.

Prolonged hospital length of stay in pediatric trauma: a model for targeted interventions.

BACKGROUND: In this study, trauma-specific risk factors of prolonged length of stay (LOS) in pediatric trauma were examined. Statistical and machine learning models were used to proffer ways to improve the quality of care of patients at risk of prolonged length of stay and reduce cost. METHODS: Data from 27 hospitals were retrieved on 81,929 hospitalizations of pediatric patients with a primary diagnosis of trauma, and for which the LOS was >24 h. Nested mixed effects model was used for simplified statistical inference, while a stochastic gradient boosting model, considering high-order statistical interactions, was built for prediction. RESULTS: Over 18.7% of the encounters had LOS >1 week. Burns and corrosion and suspected and confirmed child abuse are the strongest drivers of prolonged LOS. Several other trauma-specific and general pediatric clinical variables were also predictors of prolonged LOS. The stochastic gradient model obtained an area under the receiver operator characteristic curve of 0.912 (0.907, 0.917). CONCLUSIONS: The high performance of the machine learning model coupled with statistical inference from the mixed effects model provide an opportunity for targeted interventions to improve quality of care of trauma patients likely to require long length of stay. IMPACT: Targeted interventions on high-risk patients would improve the quality of care of pediatric trauma patients and reduce the length of stay. This comprehensive study includes data from multiple hospitals analyzed with advanced statistical and machine learning models. The statistical and machine learning models provide opportunities for targeted interventions and reduction in prolonged length of stay reducing the burden of hospitalization on families.

A Nested Mixed Effects Multicenter Model Examining the Risk Factors for Pediatric Trauma Return Visits Within 72 Hours.

BACKGROUND: Return visits within 72 h are an important metric in evaluating the performance of emergency rooms. This has not been well studied in the pediatric trauma population. We sought to determine novel risk factors for return visits to the emergency department (ED) after trauma that may assist in identifying patients most at risk of revisit. METHODS: We used the Cerner Health Facts Database to retrieve data from 34 EDs across the United States that care for pediatric trauma patients aged <15 y. The data consist of 610,845 patients and 816,571 ED encounters. We retrieved variables encompassing demographics, payor, current and past health care resource utilization, trauma diagnoses, other diagnoses/comorbidities, medications, and surgical procedures. We built a nested mixed effects logistic regression model to provide statistical inference on the return visits. RESULTS: Traumas resulting from burns and corrosion, injuries to the shoulder and arms, injuries to the hip and legs, and trauma to the head and neck are all associated with increased odds of returning to the ED. Patients suffering from poisoning relating to drugs and other biological substances and patients with trauma to multiple body regions have reduced odds of returning to the ED. Longer ED length of stay and prior health care utilization (ED or inpatient) are associated with increased odds of a return visit. The sex of the patient and payor had a statistically significant effect on the risk of a return visit to the ED within 72 h of discharge. CONCLUSIONS: Certain traumas expose patients to an increased risk for return visits to the ED and, as a result, provide opportunity for improved quality of care. Targeted interventions that include education, observation holds, or a decision to hospitalize instead of discharge home may help improve patient outcomes and decrease the rate of ED returns. LEVEL OF EVIDENCE: III (Prognostic and Epidemiology).

Evaluating the impact of coronavirus disease 2019 on asthma morbidity: A comprehensive analysis of potential influencing factors.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic period is experiencing better asthma control, fewer exacerbations, and health care utilization, with limited data on factors that could explain this phenomenon. OBJECTIVE: To confirm these improved asthma outcomes during COVID-19 and evaluate potential contributing factors. METHODS: In 18,912 pediatric patients with asthma treated in the Children's Hospital of Orange County network from 2017 to 2020, monthly asthma-related encounters and medication summaries were extracted from electronic health records, particulate matter 2.5 (PM2.5) air pollution from the California Air Resources Board, and influenza-like illness from Illness Surveillance Network for the first 6 months of each year. Changes in outcomes between January to March and April to June (post-COVID-19 shutdown in 2020) were compared with historical data using generalized estimating equations analyses for patient outcomes and generalized linear models for pollution exceedance, influenza-positive, and telehealth visit rates. RESULTS: During COVID-19, we found 78%, 90%, 68% reductions in hospitalization, emergency department visits, and exacerbations, respectively, compared with pre-COVID-19 2020, with significantly greater changes than the same time period of 2017 to 2019 and significant reductions in albuterol and inhaled corticosteroid use (P < .05). Emergency department visit reduction was not seen for African Americans. The PM2.5 and influenza rates were also significantly reduced during COVID-19 (P < .05). Increased rates in telehealth visits were greater in the publicly insured group when compared with commercially insured. CONCLUSION: Our data confirm reduced health care utilization and suggest better asthma control during COVID-19, except for African Americans. This was associated with a significant increase in telehealth visits and reductions in PM2.5 and influenza infections, but not better asthma controller adherence.

Predictors of pediatric readmissions among patients with neurological conditions.

BACKGROUND: Unplanned readmission is one of many measures of the quality of care of pediatric patients with neurological conditions. In this multicenter study, we searched for novel risk factors of readmission of patients with neurological conditions. METHODS: We retrieved hospitalization data of patients less than 18 years with one or more neurological conditions. This resulted in a total of 105,834 encounters from 18 hospitals. We included data on patient demographics, prior healthcare resource utilization, neurological conditions, number of other conditions/diagnoses, number of medications, and number of surgical procedures performed. We developed a random intercept logistic regression model using stepwise minimization of Akaike Information Criteria for variable selection. RESULTS: The most important neurological conditions associated with unplanned pediatric readmissions include hydrocephalus, inflammatory diseases of the central nervous system, sleep disorders, disease of myoneural junction and muscle, other central nervous system disorder, other spinal cord conditions (such as vascular myelopathies, and cord compression), and nerve, nerve root and plexus disorders. Current and prior healthcare resource utilization variables, number of medications, other diagnoses, and certain inpatient surgical procedures were associated with changes in odds of readmission. The area under the receiver operator characteristic curve (AUROC) on the independent test set is 0.733 (0.722, 0.743). CONCLUSIONS: Pediatric patients with certain neurological conditions are more likely to be readmitted than others. However, current and prior healthcare resource utilization remain some of the strongest indicators of readmission within this population as in the general pediatric population.

Criticality index conducted in pediatric emergency department triage.

OBJECTIVE: To develop and analyze the performance of a machine learning model capable of predicting the disposition of patients presenting to a pediatric emergency department (ED) based on triage assessment and historical information mined from electronic health records. METHODS: We retrospectively reviewed data from 585,142 ED visits at a pediatric quaternary care institution between 2013 and 2020. An extreme gradient boosting machine learning model was trained on a randomly selected training data set (50%) to stratify patients into 3 classes: (1) high criticality (patients requiring intensive care unit [ICU] care within 4 h of hospital admission, patients who died within 4 h of admission, and patients who died in the ED); (2) moderate criticality (patients requiring hospitalization without the need for ICU care); and (3) low criticality (patients discharged home). Variables considered during model development included triage vital signs, aspects of triage nursing assessment, demographics, and historical information (diagnoses, medication use, and healthcare utilization). Historical factors were limited to the 6 months preceding the index ED visit. The model was tested on a previously withheld test data set (40%), and its performance analyzed. RESULTS: The distribution of criticality among high, moderate, and low was 1.5%, 7.1%, and 91.4%, respectively. The one-versus-all area under the receiver operating characteristic (AUROC) curve for high and moderate criticality was 0.982 (95% CI 0.980, 0.983) and 0.968 (0.967, 0.969). The multi-class macro average AUROC and area under the receiver operating characteristic curve were 0.976 and 0.754. The features most integral to model performance included history of intravenous medications, capillary refill, emergency severity index level, history of hospitalization, use of a supplemental oxygen device, age, and history of admission to the ICU. CONCLUSION: Pediatric ED disposition can be accurately predicted using information available at triage, providing an opportunity to improve quality of care and patient outcomes.

Anxiety and Mood Disorders Impacting Physician Opioid Prescribing in the Pediatric Hospital Setting.

This research examined whether pediatric inpatients without an anxiety/mood disorder are more likely to receive opioids in response to pain compared to patients diagnosed with a mental health condition. Research questions were tested using cross-sectional inpatient electronic medical record data. Propensity score matching was used to match patients with a disorder with patients without the disorder (anxiety analyses: N = 2892; mood analyses: N = 1042). Although patients with anxiety and mood disorders experienced greater pain, physicians were less likely to order opioids for these patients. Analyses also disclosed an interaction of anxiety with pain-the pain-opioid relation was stronger for patients without an anxiety disorder than for patients with an anxiety diagnosis. Instead, physicians were more likely to place non-opioid analgesic orders to manage the pain of patients with anxiety disorders. Findings imply that pain management decisions might be influenced by patient's mental health.

Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients.

BACKGROUND: Pediatric patients admitted for trauma may have unique risk factors of unplanned readmission and require condition-specific models to maximize accuracy of prediction. We used a multicenter data set on trauma admissions to study risk factors and predict unplanned 7-day readmissions with comparison to the 30-day metric. METHODS: Data from 28 hospitals in the United States consisting of 82,532 patients (95,158 encounters) were retrieved, and 75% of the data were used for building a random intercept, mixed-effects regression model, whereas the remaining were used for evaluating model performance. The variables included were demographics, payer, current and past health care utilization, trauma-related and other diagnoses, medications, and surgical procedures. RESULTS: Certain conditions such as poisoning and medical/surgical complications during treatment of traumatic injuries are associated with increased odds of unplanned readmission. Conversely, trauma-related conditions, such as trauma to the thorax, knee, lower leg, hip/thigh, elbow/forearm, and shoulder/upper arm, are associated with reduced odds of readmission. Additional predictors include the current and past health care utilization and the number of medications. The corresponding 7-day model achieved an area under the receiver operator characteristic curve of 0.737 (0.716, 0.757) on an independent test set and shared similar risk factors with the 30-day version. CONCLUSIONS: Patients with trauma-related conditions have risk of readmission modified by the type of trauma. As a result, additional quality of care measures may be required for patients with trauma-related conditions that elevate their risk of readmission.

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

BACKGROUND: There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics. METHODS: We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab. RESULTS: Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models. CONCLUSION: Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

Impact of anesthesiologists on parental perioperative satisfaction scores.

BACKGROUND: While the focus on patient experience as an important outcome has significantly increased over the past decade, there is paucity of data on predictive factors associated with parental recommendation of a surgical facility to friends and family. METHODS: Data for this report were obtained from a Hospital Information System and Picker Health validated surveys completed by 538 parents whose children underwent outpatient surgery from July 2014 to March 2016. Bivariate, chi-squared, and multivariate logistic regression analysis were carried out using the Picker Health item "Would you recommend this outpatient surgical facility to your friends and family?" as the primary outcome. Demographic data and 53 Picker Health items were used as potential predictors. RESULTS: Multivariate logistic regression analysis found the following variables as independent predictors for parental recommendation: quality of perioperative communication by anesthesiologists (odds ratio [95% confidence interval]=0.23 [0.09, 0.58]); provision of information on whom to call for help after discharge (0.22 [0.07, 0.64]); child's perceived baseline health (0.37 [0.15, 0.90]); and ill-informed staff about child's procedure (0.30 [0.21, 0.79]). Variables such as child's pain and child's nausea and vomiting were not predictive for referral pattern. CONCLUSION: Parental recommendation of a surgical facility to friends and family depends on a number of variables with the quality of perioperative communication with the anesthesiologist being the most predictive item.