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Kids’ Inpatient Database: Empowering Scientific Discovery. Jay G. Berry MD MPH Complex Care Service, Cerebral Palsy Program, Program for Patient Safety and Quality Division of General Pediatrics , Children’s Hospital Boston, Boston, MA .
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Kids’ Inpatient Database:EmpoweringScientific Discovery Jay G. Berry MD MPH Complex Care Service, Cerebral Palsy Program, Program for Patient Safety and Quality Division of General Pediatrics, Children’s Hospital Boston, Boston, MA
2:30 am, December 14th, 2003 • First KID Exposure • Setting • Pediatrics resident, on-call • Admitting patients with Dr. Raj Srivastava • Patient with hypoplastic left heart syndrome • Surgical complication • Related to surgical team inexperience
2:30 am, December 14th, 2003 • First KID Exposure • ICD-9 code book • KID 1997 • Modeling: mortality variation among hospitals
2:30 am, December 14th, 2003 • First KID Exposure • ICD-9 code book • KID 1997 • Modeling: mortality variation among hospitals Higher mortality rates for children undergoing surgery for hypoplastic left heart syndrome in low volume and non-teaching hospitals.
KID: Personal Impact • Power of large, administrative datasets to study outcomes and utilization of children with rare diseases • Entrance into the world of pediatric quality of care and health services research
My Clinical and Research Interest • Children with medical complexity • Chronic health conditions • Multiple co-morbidities • Surgery collaboration • Predict outcomes • Develop care plans
Predicting Outcomes • Children with medical complexity • Attributes • Low prevalence • Unique combinations of co-morbid conditions • Existing outcome evidence • Single institution-based • Longitudinal data, over multiple decades
Improving Outcome Prediction • Kids’ Inpatient Database • Powered to study rare conditions • Robust stratified sample • Nationally-representative • Elements for co-morbidity-outcome analyses • Diagnoses, procedures • Mortality, complications • Inpatient utilization
Tracheotomy in Children Indication Overcome life-limiting respiratory compromise Rise in patient complexity Multiple co-morbid conditions Major caregiving burden Life disruption
Mortality Following Tracheotomy Existing evidence 1-3% early mortality rate Single institutions Mortality may be under-estimated Rising patient complexity Presence of tenuous co-morbid conditions
Tracheotomy Mortality Analysis Kids’ Inpatient Database 2006 Tracheotomy ICD-9 codes In-hospital mortality Partition, Regression Tree Modeling Demographic and co-morbid conditions Characteristic combinations and mortality
Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6% Age ≥ 1 year 12% Age < 1 year 30%
Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6% Age ≥ 1 year 12% Age < 1 year 30%
Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6% Age ≥ 1 year 12% Age < 1 year 30%
KID Impact for Children Undergoing Tracheotomy • Bringing evidence to the bedside • Individualizing outcome prediction • Counseling families of risk and benefit • Increased attention to at-risk patients
Keep it coming! • The HCUP-KID empowers scientific discovery that is leading to improvements in care for children! • Future data element expansion will enhance it’s power!
Thank you • AHRQ and the HCUP team • Pamela Owens and Anne Elixhauser • Raj Srivastava and Don Goldmann