1. 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

2. 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

3. 2:30 am, December 14th, 2003 First KID Exposure –ICD-9 code book –KID 1997 –Modeling: mortality variation among hospitals

4. 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.

5. 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

6. My Clinical and Research Interest Children with medical complexity –Chronic health conditions –Multiple co-morbidities Surgery collaboration –Predict outcomes –Develop care plans

7. 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

8. 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

9. Tracheotomy in Children Indication –Overcome life-limiting respiratory compromise Rise in patient complexity –Multiple co-morbid conditions Major caregiving burden –Life disruption

10. 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

11. 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

12. KID 2006 4,751 Tracheotomy Hospitalizations Neurological Impairment 46% Chronic Lung Disease 44% Upper Airway Anomaly 28% Congenital Heart Disease 19% Prematurity 13% ≥ 2 Clinical Conditions 67%

13. Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Age < 1 year 30% Age ≥ 1 year 12% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6%

14. Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Age < 1 year 30% Age ≥ 1 year 12% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6%

15. Tracheotomy Mortality Regression Tree All Patients Mortality = 9% Cong. Heart Disease (-) 6% Cong. Heart Disease (+) 19% Age < 1 year 30% Age ≥ 1 year 12% Prematurity (-) 5% Prematurity (+) 17% Airway Anomaly (+) 7% Airway Anomaly (-) 27% Airway Anomaly (+) 2% Airway Anomaly (-) 6%

16. 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

17. 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!

18. Thank you AHRQ and the HCUP team Pamela Owens and Anne Elixhauser Raj Srivastava and Don Goldmann