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Awareness of a patient's clinical status during hospitalization is a primary responsibility for hospital providers. One tool to assess status is the Rothman Index (RI), a validated measure of patient condition for adults, based on empirically derived relationships between 1-year post-discharge mortality and each of 26 clinical measurements available in the electronic medical record. However, such an approach cannot be used for pediatrics, where the relationships between risk and clinical variables are distinct functions of patient age, and sufficient 1-year mortality data for each age group simply do not exist. We report the development and validation of a new methodology to use adult mortality data to generate continuously age-adjusted acuity scores for pediatrics. Clinical data were extracted from EMRs at three pediatric hospitals covering 105,470 inpatient visits over a 3-year period. The RI input variable set was used as a starting point for the development of the pediatric Rothman Index (pRI). Age-dependence of continuous variables was determined by plotting mean values versus age. For variables determined to be age-dependent, polynomial functions of mean value and mean standard deviation versus age were constructed. Mean values and standard deviations for adult RI excess risk curves were separately estimated. Based on the "find the center of the channel" hypothesis, univariate pediatric risk was then computed by applying a z-score transform to adult mean and standard deviation values based on polynomial pediatric mean and standard deviation functions. Multivariate pediatric risk is estimated as the sum of univariate risk. Other age adjustments for categorical variables were also employed. Age-specific pediatric excess risk functions were compared to age-specific expert-derived functions and to in-hospital mortality. AUC for 24-h mortality and pRI scores prior to unplanned ICU transfers were computed. Age-adjusted risk functions correlated well with similar functions in Bedside PEWS and PAWS. Pediatric nursing data correlated well with risk as measured by mortality odds ratios. AUC for pRI for 24-h mortality was 0.93 (0.92, 0.94), 0.93 (0.93, 0.93) and 0.95 (0.95, 0.95) at the three pediatric hospitals. Unplanned ICU transfers correlated with lower pRI scores. Moreover, pRI scores declined prior to such events. A new methodology to continuously age-adjust patient acuity provides a tool to facilitate timely identification of physiologic deterioration in hospitalized children., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: To assess the relationship between children's hospital readmission and the performance of child health systems in the states in which hospitals are located., Study Design: We conducted a retrospective cohort study of 197,744 patients 2 to 18 years old from 39 children's hospitals located in 24 states in the United States in 2005. Subjects were observed for a year after discharge for readmission to the same hospital. The odds of readmission were modeled on the basis of patient-level characteristics and state child health system performance as ranked by the Commonwealth Fund., Results: A total of 1.8% of patients were readmitted within a week, 4.8% within a month, and 16.3% within 365 days. After adjustment for patient-level characteristics, the probability of readmission varied significantly between states (P=.001), and the likelihood of readmission during the ensuing year increased as the states' health system performance ranking improved. States in the best ranking quartile had a 2.03% higher readmission rate than states in the lowest quartile (P=.02); the same directional relationship was observed for readmission intervals from 1 to 365 days after discharge., Conclusions: Hospital readmission rates are significantly related to the performance of the surrounding health care system.

Background: Children with complex chronic conditions depend on both their families and systems of pediatric health care, social services, and financing. Investigations into the workings of this ecology of care would be advanced by more accurate methods of population-level predictions of the likelihood for future hospitalization., Methods: This was a retrospective cohort study. Hospital administrative data were collected from 38 children's hospitals in the United States for the years 2003-2005. Participants included patients between 2 and 18 years of age discharged from an index hospitalization during 2004. Patient characteristics documented during the index hospitalization or any previous hospitalization during the preceding 365 days were included. The main outcome measure was readmission to the hospital during the 365 days after discharge from the index admission., Results: Among the cohort composed of 186856 patients discharged from the participating hospitals during 2004, the mean age was 9.2 years, with 54.4% male and 52.9% identified as non-Hispanic white. A total of 17.4% were admitted during the previous 365 days, and among those discharged alive (0.6% died during the admission), 16.7% were readmitted during the ensuing 365 days. The final readmission model exhibited a c statistic of 0.81 across all hospitals, with a range from 0.76 to 0.84 for each hospital. Bootstrap-based assessments demonstrated the stability of the final model., Conclusions: Accurate population-level prediction of hospital readmissions is possible, and the resulting predicted probability of hospital readmission may prove useful for health services research and planning.