Swati DiDonato, MD

Abstract

The goal of this report was to find the frequency of synchronous airway lesions (SAL) identified during microdirect laryngoscopy and bronchoscopy (MDLB) that influenced treatment decisions beyond the information provided by drug induced sleep endoscopy (DISE) alone in children with obstructive sleep apnea (OSA) at a tertiary care pediatric hospital.This was a retrospective chart review of all pediatric patients who underwent drug induced sleep endoscopy in conjunction with direct laryngoscopy and bronchoscopy as part of a comprehensive airway evaluation for obstructive sleep apnea at a tertiary care pediatric hospital.Three hundred thirty-five patients with obstructive sleep apnea were evaluated with both sleep endoscopy and direct laryngoscopy with bronchoscopy. Five percent of patients had SAL identified on MDLB contributing to airway obstruction. Three patients (0.9%) who underwent MDLB for OSA required surgical correction of SAL that was identified.In a limited subset of patients, direct laryngoscopy with bronchoscopy provides additional positive findings to aid with treatment planning for obstructive sleep apnea.

View details for PubMedID 29859583

Abstract

Prostate cancer is an extremely prevalent cause of morbidity and mortality among American men. Several different treatments exist, but differences in utilization between these treatments are not well understood.We performed an observational study using administrative datasets linked to hospital survey data, which included non-metastatic prostate cancer patients receiving robot-assisted radical prostatectomy (RARP) or open radical prostatectomy (ORP) in California, Florida, or New York from 2009-2011. We developed two hierarchical regression models with fixed effect accounting for hospital clustering and physician clustering to determine factors associated with utilization of RARP versus ORP at hospitals offering robotic surgery.A total of 36,694 patients were identified, with 77.13% receiving RARP and 22.87% receiving ORP. African American patients had lower RARP rates than White patients (OR = 0.80, p < 0.001). Patients using Medicare (OR = 0.91, p = 0.028), Medicaid (OR = 0.68, p < 0.001), or self-pay (OR = 0.72, p = 0.046) had lower RARP rates than patients using private insurance. Patients in Florida had lower RARP rates than patients in California (OR = 0.48, p = 0.010). Patients treated at teaching hospitals had lower RARP rates than patients treated at non-teaching hospitals (OR = 0.50, p = 0.006). The average cost of RARP was $13,614.83, and the average cost of ORP was $12,167.44 (p < 0.001).This population based study suggests that both patient and hospital characteristics are associated with utilization of RARP versus ORP in hospitals where robotic surgery is offered.

View details for PubMedID 27347621

Abstract

Prostate cancer is an extremely prevalent cause of morbidity and mortality among American men. Several different treatments exist, but differences in utilization between these treatments are not well understood.We performed an observational study using administrative datasets linked to hospital survey data, which included non-metastatic prostate cancer patients receiving robot-assisted radical prostatectomy (RARP) or open radical prostatectomy (ORP) in California, Florida, or New York from 2009-2011. We developed two hierarchical regression models with fixed effect accounting for hospital clustering and physician clustering to determine factors associated with utilization of RARP versus ORP at hospitals offering robotic surgery.A total of 36,694 patients were identified, with 77.13% receiving RARP and 22.87% receiving ORP. African American patients had lower RARP rates than White patients (OR = 0.80, p < 0.001). Patients using Medicare (OR = 0.91, p = 0.028), Medicaid (OR = 0.68, p < 0.001), or self-pay (OR = 0.72, p = 0.046) had lower RARP rates than patients using private insurance. Patients in Florida had lower RARP rates than patients in California (OR = 0.48, p = 0.010). Patients treated at teaching hospitals had lower RARP rates than patients treated at non-teaching hospitals (OR = 0.50, p = 0.006). The average cost of RARP was $13,614.83, and the average cost of ORP was $12,167.44 (p < 0.001).This population based study suggests that both patient and hospital characteristics are associated with utilization of RARP versus ORP in hospitals where robotic surgery is offered.

View details for Web of Science ID 000379635800006

Abstract

Electronic health records (EHRs) were implemented to improve quality of care and patient outcomes. This study assessed the relationship between EHR-adoption and patient outcomes.We performed an observational study using State Inpatient Databases linked to American Hospital Association survey, 2011. Surgical and medical patients from 6 large, diverse states were included. We performed univariate analyses and developed hierarchical regression models relating level of EHR utilization and mortality, readmission rates, and complications. We evaluated the effect of EHR adoption on outcomes in a difference-in-differences analysis, 2008 to 2011.Medical and surgical patients sought care at hospitals reporting no EHR (3.5%), partial EHR (55.2%), and full EHR systems (41.3%). In univariate analyses, patients at hospitals with full EHR had the lowest rates of inpatient mortality, readmissions, and Patient Safety Indicators followed by patients at hospitals with partial EHR and then patients at hospitals with no EHR (P<0.05). However, these associations were not robust when accounting for other patient and hospital factors, and adoption of an EHR system was not associated with improved patient outcomes (P>0.05).These results indicate that patients receiving medical and surgical care at hospitals with no EHR system have similar outcomes compared to patients seeking care at hospitals with a full EHR system, after controlling for important confounders.To date, we have not yet seen the promised benefits of EHR systems on patient outcomes in the inpatient setting. EHRs may play a smaller role than expected in patient outcomes and overall quality of care.

View details for DOI 10.1097/MD.0000000000003332

View details for Web of Science ID 000376927000010

View details for PubMedID 27175631

View details for PubMedCentralID PMC4902473

Abstract

This study quantified professional athletes' endorsement of food and beverages, evaluated the nutritional quality of endorsed products, and determined the number of television commercial exposures of athlete-endorsement commercials for children, adolescents, and adults.One hundred professional athletes were selected on the basis of Bloomberg Businessweek's 2010 Power 100 rankings, which ranks athletes according to their endorsement value and prominence in their sport. Endorsement information was gathered from the Power 100 list and the advertisement database AdScope. Endorsements were sorted into 11 endorsement categories (eg, food/beverages, sports apparel). The nutritional quality of the foods featured in athlete-endorsement advertisements was assessed by using a Nutrient Profiling Index, whereas beverages were evaluated on the basis of the percentage of calories from added sugar. Marketing data were collected from AdScope and Nielsen.Of 512 brands endorsed by 100 different athletes, sporting goods/apparel represented the largest category (28.3%), followed by food/beverages (23.8%) and consumer goods (10.9%). Professional athletes in this sample were associated with 44 different food or beverage brands during 2010. Seventy-nine percent of the 62 food products in athlete-endorsed advertisements were energy-dense and nutrient-poor, and 93.4% of the 46 advertised beverages had 100% of calories from added sugar. Peyton Manning (professional American football player) and LeBron James (professional basketball player) had the most endorsements for energy-dense, nutrient-poor products. Adolescents saw the most television commercials that featured athlete endorsements of food.Youth are exposed to professional athlete endorsements of food products that are energy-dense and nutrient-poor.

View details for DOI 10.1542/peds.2013-0093

View details for Web of Science ID 000326475000047

View details for PubMedID 24101762

View details for DOI 10.1017/S1368980012003187

View details for Web of Science ID 000307187000002

View details for PubMedID 22687144

View details for DOI 10.1161/01.HYP.0000240048.67377.e4

View details for Web of Science ID 000243571200185