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Purpose: We sought to develop and validate an Anticipated Surveillance Requirement Prediction Instrument (ASRI) for prediction of prolonged postanesthesia care unit length of stay (PACU-LOS, more than four hours) after ambulatory surgery. Methods: We analyzed hospital registry data from patients who received anesthesia care in ambulatory surgery centres (ASCs) of university-affiliated hospital networks in New York, USA (development and internal validation cohort [n= 183,711]) and Massachusetts, USA (validation cohort [n= 148,105]). We used stepwise backwards elimination to create ASRI. Results: The model showed discriminatory ability in the development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.82 to 0.83), 0.82 (95% CI, 0.81 to 0.83), and 0.80 (95% CI, 0.79 to 0.80), respectively. In cases started in the afternoon, ASRI scores ≥ 43 had a total predicted risk for PACU stay past 8 p.m. of 32% (95% CI, 31.1 to 33.3) vs8% (95% CI, 7.9 to 8.5) compared with low score values (P-for-interaction < 0.001), which translated to a higher direct PACU cost of care of USD 207 (95% CI, 194 to 2,019; model estimate, 1.68; 95% CI, 1.64 to 1.73; P< 0.001) The effects of using the ASRI score on PACU use efficiency were greater in a free-standing ASC with no limitations on PACU bed availability. Conclusion: We developed and validated a preoperative prediction tool for prolonged PACU-LOS after ambulatory surgery that can be used to guide scheduling in ambulatory surgery to optimize PACU use during normal work hours, particularly in settings without limitation of PACU bed availability.

Metastasis to the spinal column is a common complication of malignancy, potentially causing pain and neurologic injury. An automated system to identify and refer patients with spinal metastases can help overcome barriers to timely treatment. We describe the training, optimization and validation of a natural language processing algorithm to identify the presence of vertebral metastasis and metastatic epidural cord compression (MECC) from radiology reports of spinal MRIs.

AbstractBackground:Inpatient surgical site infections (SSIs) cause morbidity in children. The SSI rate among pediatric ambulatory surgery patients is less clear. To fill this gap, we conducted a multiple-institution, retrospective epidemiologic study to identify incidence, risk factors, and outcomes.Methods:We identified patients aged <22 years with ambulatory visits between October 2010 and September 2015 via electronic queries at 3 medical centers. We performed sample chart reviews to confirm ambulatory surgery and adjudicate SSIs. Weighted Poisson incidence rates were calculated. Separately, we used case–control methodology using multivariate backward logistical regression to assess risk-factor association with SSI.Results:In total, 65,056 patients were identified by queries, and we performed complete chart reviews for 13,795 patients; we identified 45 SSIs following ambulatory surgery. The weighted SSI incidence following pediatric ambulatory surgery was 2.00 SSI per 1,000 ambulatory surgeries (95% confidence interval [CI], 1.37–3.00). Integumentary surgeries had the highest weighted SSI incidence, 3.24 per 1,000 ambulatory surgeries (95% CI, 0.32–12). The following variables carried significantly increased odds of infection: clean contaminated or contaminated wound class compared to clean (odds ratio [OR], 9.8; 95% CI, 2.0–48), other insurance type compared to private (OR, 4.0; 95% CI, 1.6–9.8), and surgery on weekend day compared to weekday (OR, 30; 95% CI, 2.9–315). Of the 45 instances of SSI following pediatric ambulatory surgery, 40% of patients were admitted to the hospital and 36% required a new operative procedure or bedside incision and drainage.Conclusions:Our findings suggest that morbidity is associated with SSI following ambulatory surgery in children, and we also identified possible targets for intervention.

AbstractObjective:Ambulatory healthcare-associated infections (HAIs) occur frequently in children and are associated with morbidity. Less is known about ambulatory HAI costs. This study estimated additional costs associated with pediatric ambulatory central-line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTI), and surgical site infections (SSIs) following ambulatory surgery.Design:Retrospective case-control study.Setting:Four academic medical centers.Patients:Children aged 0–22 years seen between 2010 and 2015 and at risk for HAI as identified by electronic queries.Methods:Chart review adjudicated HAIs. Charges were obtained for patients with HAIs and matched controls 30 days before HAI, on the day of, and 30 days after HAI. Charges were converted to costs and 2015 USD. Mixed-effects linear regression was used to estimate the difference-in-differences of HAI case versus control costs in 2 models: unrecorded charge values considered missing and a sensitivity analysis with unrecorded charge considered $0.Results:Our search identified 177 patients with ambulatory CLABSIs, 53 with ambulatory CAUTIs, and 26 with SSIs following ambulatory surgery who were matched with 382, 110, and 75 controls, respectively. Additional cost associated with an ambulatory CLABSI was $5,684 (95% confidence interval [CI], $1,005–$10,362) and $6,502 (95% CI, $2,261–$10,744) in the 2 models; cost associated with a CAUTI was $6,660 (95% CI, $1,055, $12,145) and $2,661 (95% CI, −$431 to $5,753); cost associated with an SSI following ambulatory surgery at 1 institution only was $6,370 (95% CI, $4,022–$8,719).Conclusions:Ambulatory HAI in pediatric patients are associated with significant additional costs. Further work is needed to reduce ambulatory HAIs.

AbstractObjective:Catheter-associated urinary tract infections (CAUTIs) occur frequently in pediatric inpatients, and they are associated with increased morbidity and cost. Few studies have investigated ambulatory CAUTIs, despite at-risk children utilizing home urinary catheterization. This retrospective cohort and case-control study determined incidence, risk factors, and outcomes of pediatric patients with ambulatory CAUTI.Design:Broad electronic queries identified potential patients with ambulatory urinary catheters, and direct chart review confirmed catheters and adjudicated whether ambulatory CAUTI occurred. CAUTI definitions included clean intermittent catheterization (CIC). Our matched case-control analysis assessed risk factors.Setting:Five urban, academic medical centers, part of the New York City Clinical Data Research Network.Patients:Potential patients were age <22 years who were seen between October 2010 and September 2015.Results:In total, 3,598 eligible patients were identified; 359 of these used ambulatory catheterization (representing186,616 ambulatory catheter days). Of these, 63 patients (18%) experienced 95 ambulatory CAUTIs. The overall ambulatory CAUTI incidence was 0.51 infections per 1,000 catheter days (1.35 for indwelling catheters and 0.47 for CIC; incidence rate ratio, 2.88). Patients with nonprivate medical insurance (odds ratio, 2.5; 95% confidence interval, 1.1–6.3) were significantly more likely to have ambulatory CAUTIs in bivariate models but not multivariable models. Also, 45% of ambulatory CAUTI resulted in hospitalization (median duration, 3 days); 5% resulted in intensive care admission; 47% underwent imaging; and 88% were treated with antibiotics.Conclusions:Pediatric ambulatory CAUTIs occur in 18% of patients with catheters; they are associated with morbidity and healthcare utilization. Ambulatory indwelling catheter CAUTI incidence exceeded national inpatient incidence. Future quality improvement research to reduce these harmful infections is warranted.

Introduction: Acute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric sickle cell disease (SCD). Fifty to thirty percent of all pediatric SCD admissions develop ACS, with each episode potentially causing irreversible lung damage. Although there are some known risk factors for developing ACS (e.g., history of asthma), often this complication occurs unexpectedly. Our hypothesis was that a machine learning model could be developed to predict progression to ACS. Methods: A retrospective cohort study was designed to develop this prediction model. Data was abstracted from the electronic health record (EHR) from a single institution for all pediatric SCD admissions from 0-21 years old between June 1, 2016, and December 31, 2022. Inputs into the model included demographics (e.g., age, genotype), vital signs (e.g., heart rate, oxygen saturation), laboratory values (e.g., hemoglobin, neutrophil count), medication orders (e.g., albuterol, oxycodone) and additional co-morbidities (e.g., asthma, obstructive sleep apnea). The inputs had maximum and minimum values based on normal ranges by age. The output was a diagnosis of ACS as a continuous dependent variable to develop a continuous likelihood estimation score from 0 to 1. A positive output of ACS was defined as 2 doses of the antibiotic azithromycin and a chest x-ray ordered in the same encounter. The negative output included all patients who did not meet criteria for a positive output as defined above. Both positive and negative cohorts for ACS were included in the model. The time of ACS diagnosis was when the first dose of azithromycin was ordered. All ACS encounters were verified by chart review. A random forest model was implemented using Sklearn Python library. Different depth of trees was experimented between 1 to 24. A prediction for ACS was calculated every 6 hours after admission to the inpatient setting. Eighty percent of the data was used for training and twenty percent for testing. Results: Two thousand two hundred and sixty encounters were included in the negative cohort and 512 encounters were included in the positive cohort. One thousand four hundred and fourteen (51%) were male patients and 1358 were female patients (49%). The maximum depth of the tree was determined to be 10. Sixty-five percent of the positive cohort developed ACS 24 hours after the admission time. The median time that the random forest model predicted ACS was 18 hours before the diagnosis was determined in the chart. The model had a sensitivity of 80% and specificity of 64% at the cutoff point of 0.17. The area under the receiver operating characteristic (ROC) curve was 71%. The model's negative predictive value was 92% and the positive predictive value was 36% of diagnosing ACS. The features that were important predictors in the model were age, height, weight, pulse, respiratory rate, presence of hypoxia, temperature, neutrophil count, hemoglobin, absolute reticulocyte count and reticulocyte percent. Conclusion: A machine learning model demonstrated markers of hemolysis and vital sign changes were important to predict ACS in pediatric patients. This is the first model utilizing machine learning techniques in pediatric SCD to predict ACS. Detecting patients who are a higher risk for ACS could impact treatment options and approach to inpatient management.

Background:Multiple myeloma (MM) is a bone marrow-based, multifocal plasma cell neoplasm associated with an M-protein in serum and / or urine. Events mediating progression from the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) to MM are not clearly understand. Numerous genetic events and environmental factors appear to trigger the progression of MM. Although there have been many studies on occupational and toxin exposures as risk factors to the development of non-Hodgkin's lymphoma, there are few studies that focus on military service and toxin exposures as potential risk factors to the development and progression of MM.

Seismic loads may damage municipal solid waste (MSW) landfills through the relative movements within the landfill system. These movements can disrupt performance of drainage and gas collection systems, thereby resulting in environmental pollution. The smooth synthetic materials might be placed beneath the structures to provide seismic protection by absorbing the imparted energy of earthquakes through the sliding mechanism. It has been found that a high strength geomembrane placed over the other smooth and lubricated geomembrane sheets constitutes an efficient seismic liner. In the present study, experimental investigations were conducted to evaluate role of in-soil base isolation on seismic response of the Tehran MSW landfill. Results of geophysical and geotechnical investigations in the landfill site are presented in detail. Shaking table tests were conducted on the MSW embankment isolated by semi-elliptic shaped liners and subjected to harmonic sinusoidal base excitations. The results for the isolated and non-isolated cases are compared in terms of permanent displacement and seismic response. It has been observed that at all elevations the spectral accelerations within the waste decreased by base isolation, especially for the more intense excitations. Results of the present study demonstrate a suitable application of geosynthetic liners for seismic retrofitting of landfills.

“Public health surveillance (PHS) is the ongoing and systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health.” As information technology gains acceptance as a core element of public health practice, many approaches to the design of PHS systems have been proposed, much has been spent implementing them, and expectations have been high. Unfortunately, the systems implemented so far have been criticized as having not met expectations, especially in the domain of early detection and bioterrorism readiness, or so-called syndromic surveillance (The term “syndromic surveillance” applies to monitoring health-related data that precede diagnosis to signal a sufficient probability of a case or an outbreak that warrants public health response.). There are no fully established frameworks to enable seamless interoperability, information sharing, and collaboration among PHS stakeholders and the technological and infrastructural requirements to fulfill the grand vision of initiatives such as the Public Health Information Network and National Health Information Network are poorly investigated and documented.In this article, we examine the current state of the conceptualization, design, analysis, and implementation of PHS systems from a translational informatics perspective. Although most examples in this article are informed by the needs of public health preparedness (syndromic and bioterrorism detection and response), we believe the framework we introduce is generalizable and applicable to the broader context of PHS systems. We also apply concepts from cognitive science and knowledge engineering to suggest directions for improvement and further research.

“Public health surveillance (PHS) is the ongoing and systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health.” As information technology gains acceptance as a core element of public health practice, many approaches to the design of PHS systems have been proposed, much has been spent implementing them, and expectations have been high. Unfortunately, the systems implemented so far have been criticized as having not met expectations, especially in the domain of early detection and bioterrorism readiness, or so-called syndromic surveillance (The term “syndromic surveillance” applies to monitoring health-related data that precede diagnosis to signal a sufficient probability of a case or an outbreak that warrants public health response.). There are no fully established frameworks to enable seamless interoperability, information sharing, and collaboration among PHS stakeholders and the technological and infrastructural requirements to fulfill the grand vision of initiatives such as the Public Health Information Network and National Health Information Network are poorly investigated and documented.

There is limited clinical patient data comparing the first and second waves of the coronavirus disease 2019 (COVID-19) in the United States and the effects of a COVID-19 resurgence on different age, racial and ethnic groups. We compared the first and second COVID-19 waves in the Bronx, New York, among a racially and ethnically diverse population.

The essential oils were isolated by steam distillation from the aerial parts of A. aucheriBoiss., A. santolinaSchrenk and A. sieberiBesser. The oils were analyzed by capillary GC and GC–MS. The highest oil yield was obtained from A. sieberi(1.02%). The main constituents of the oils were as follows: A. aucheri, verbenone (21.5%), camphor (21.0%) 1,8‐cineole (8.3%) and trans‐verbenol (8.1%); A. santolina, neryl acetate (13.4%), bornyl acetate (10.9%), trans‐verbenol (9.9%), lavendulol (8.8%), linalool (6.9%) and 1,8‐cineole (6.5%); A. sieberi, camphor (49.3%), 1,8‐cineole (11.1%) and bornyl acetate (5.8%). Copyright © 2002 John Wiley & Sons, Ltd.