Prediction of pediatric death in the year after hospitalization: a population-level retrospective cohort study
Child; Female; Humans; infant; Male; Young Adult; Cohort Studies; Patient Discharge; Pediatrics; Hospital Mortality; Logistic Models; Prognosis; adolescent; Preschool; infant; Models; Newborn; retrospective studies; Theoretical; mortality; Pennsylvania/epidemiology
BACKGROUND: The study of how the quality of pediatric end-of-life care varies across systems of health care delivery and financing is hampered by lack of methods to adjust for the probability of death in populations of ill children. OBJECTIVE: To develop a prognostication models using administratively available data to predict the probability of in-hospital and 1-year postdischarge death. METHODS: Retrospective cohort study of 0-21 year old patients admitted to Pennsylvania hospitals from 1994-2001 and followed for 1-year postdischarge mortality, assessing logistic regression models ability to predict in-hospital and 1-year postdischarge deaths. RESULTS: Among 678,365 subjects there were 2,202 deaths that occurred during the hospitalization (0.32% of cohort) and 860 deaths that occurred 365 days or less after hospital discharge (0.13% of cohort). The model predicting hospitalization deaths exhibited a C statistic of 0.91, with sensitivity of 65.9% and specificity of 92.9% at the 99th percentile cutpoint; while the model predicting 1-year postdischarge deaths exhibited a C statistic of 0.92, with sensitivity of 56.1% and specificity of 98.4% at the 99th percentile cutpoint. CONCLUSIONS: Population-level mortality prognostication of hospitalized children using administratively available data is feasible, assisting the comparison of health care services delivered to children with the highest probability of dying during and after a hospital admission.
2009
Feudtner C; Hexem KR; Shabbout M; Feinstein JA; Sochalski J; Silber JH
Journal Of Palliative Medicine
2009
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1089/jpm.2008.0206" target="_blank" rel="noreferrer">10.1089/jpm.2008.0206</a>
Validation of a decision rule identifying febrile young girls at high risk for urinary tract infection
Child; Female; Humans; Decision Support Techniques; Risk Factors; European Continental Ancestry Group; Sensitivity and Specificity; Hospitals; Case-Control Studies; Emergency Service; Preschool; P.H.S.; Research Support; U.S. Gov't; infant; retrospective studies; Pediatric/statistics & numerical data; Pennsylvania/epidemiology; ROC Curve; Area Under Curve; Bacteriuria/diagnosis/microbiology; Colony Count; False Positive Reactions; Fever/etiology; Hospital/statistics & numerical data; Microbial; Urinary Tract Infections/diagnosis/epidemiology
OBJECTIVE: To validate a previously published clinical decision rule to predict risk of urinary tract infection in febrile young girls. METHODS: We performed a retrospective case-control study at a children's hospital emergency department in a different city than that in which the original derivation study took place. Girls younger than 2 years in whom urinalysis and urine culture were performed for evaluation of fever were eligible. Cases consisted of all patients with a positive urine culture result, defined as 50,000 or more colony-forming units per milliliter of a urinary tract pathogen (n = 98). A random sample of patients with a negative urine culture result (n = 114) was also selected as controls. The clinical prediction rule included five risk factors: age younger than 12 months, white race, temperature of 39.0 degrees C or higher, absence of any other potential source of fever, and fever for 2 days or more. The sensitivity and false-positive rate of this rule were calculated at different cutoff values. RESULTS: The overall discriminative ability of the rule, as indicated by the area under the receiver-operator characteristic curve (AUC), was similar in this validation sample (AUC = 0.72) to that in the original study (AUC = 0.76). However, in the validation sample, the presence of three or more risk factors (rather than two or more as in the original study) appeared to be the optimum cutoff to define a positive rule, which results in an indication for obtaining further diagnostic testing (sensitivity, 88% [95% CI, 79-94%]; false-positive rate, 70% [95% CI, 61-79%]). CONCLUSION: A simple clinical decision rule previously developed to predict urinary tract infection based on five risk factors performs similarly in a different patient population.
2003
Gorelick MH; Hoberman A; Kearney D; Wald E; Shaw KN
Pediatric Emergency Care
2003
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Journal Article
<a href="http://doi.org/10.1097/01.pec.0000081238.98249.40" target="_blank" rel="noreferrer">10.1097/01.pec.0000081238.98249.40</a>