A Novel Multispecialty Surgical Risk Score for Children
ABSTRACT BACKGROUND AND OBJECTIVE:There is a lack of broadly applicable measures for risk adjustment in pediatric surgical patients necessary for improving outcomes and patient safety. Our objective was to develop a risk stratification model that predicts mortality after surgical operations in children.METHODS:The model was created by using inpatient databases from 1988 to 2006. Patients younger than 18 years who underwent an inpatient surgical procedure as identified by using the International Classification of Diseases, Ninth Revision, Clinical Modification, coding were included. A 7-point scale was developed with 70 variables selected for their predictive value for mortality using multivariate analysis. This model was evaluated with receiver operating characteristic (ROC) analysis and compared with the Charlson Comorbidity Index (CCI) in two separate validation data sets.RESULTS:A total of 2 087 915 patients were identified in the training data set. Generated risk scores positively correlated with inpatient mortality. In the training data set, the ROC was 0.949 (95% confidence interval [CI]: 0.947, 0.950). In the first validation data set, the ROC was 0.959 (95% CI: 0.952, 0.967) compared with the CCI ROC of 0.596 (95% CI: 0.575, 0.616). In the second validation data set, the ROC was 0.901 (95% CI: 0.885, 0.917) and the CCI ROC was 0.587 (95% CI: 0.562, 0.611).CONCLUSIONS:This study depicts creation of a broadly applicable model for risk adjustment that predicts inpatient mortality with more reliability than current risk indexes in pediatric surgical patients. This risk index will allow comorbidity-adjusted outcomes broadly in pediatric surgery.
SourceAvailable from: Dirk T Ubbink[Show abstract] [Hide abstract]
ABSTRACT: Background The actual amount of care hospitalised patients need is unclear. A model to quantify the demand for hospital care services among various clinical specialties would avail healthcare professionals and managers to anticipate the demand and costs for clinical care.Methods Three medical specialties in a Dutch university hospital participated in this prospective time and motion study. To include a representative sample of patients admitted to clinical wards, the most common admission diagnoses were selected from the most recent update of the national medical registry (LMR) of ICD-10 admission diagnoses. The investigators recorded the time spent by physicians and nurses on patient care. Also the costs involved in medical and nursing care, (surgical) interventions, and diagnostic procedures as an estimate of the demand for hospital care services per hospitalised patient were calculated and cumulated. Linear regression analysis was applied to determine significant factors including patient and healthcare outcome characteristics.ResultsFifty patients on the Surgery (19), Pediatrics (17), and Obstetrics & Gynecology (14) wards were monitored during their hospitalization. Characteristics significantly associated with the demand for healthcare were: polypharmacy during hospitalization, complication severity level, and whether a surgical intervention was performed.ConclusionsA set of predictors of the demand for hospital care services was found applicable to different clinical specialties. These factors can all be identified during hospitalization and be used as a managerial tool to monitor the patients¿ demand for hospital care services and to detect trends in time.BMC Health Services Research 01/2015; 15(1):15. DOI:10.1186/s12913-014-0674-2 · 1.66 Impact Factor
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ABSTRACT: Background. There is an ongoing debate among pediatric surgeons regarding the need or lack thereof to centralize the surgical care of children to high-volume children's centers. Risk-adjusted comparisons of hospitals performing pediatric surgery are needed. Methods. Admissions from 2006 to 2010 from two national administrative databases were analyzed. Only nontrauma pediatric patients undergoing a noncardiac surgical procedure were included. Risk-adjustment was performed with a validated International Classification of Diseases, 9th Revision code-based tool. Hospitals were grouped into metropolitan regions using the first three digits of their zip code. Poorly performing outlier hospitals were defined by an odds ratio >1 and P value <.05 for mortality compared with the center with the greatest pediatric operative volume in that same region. Results. Information was obtained from 415,546 pediatric surgical admissions, and 173 hospitals in 55 regions were compared. A total of 18 poor performing hospitals (adjusted odds ratio, range 1.91-35.95) in 15 regions were identified. Mortality in poor performers ranged from 1.11% to 10.19% whereas that in the high-volume reference centers was 0.37-2.41%; A subset analysis in patients <1 year of age showed 37 poor performers in 46 regions. Median number of surgical admissions was 345 (interquartile range 152-907) for nonoutlier and 240 (interquartile range 135-566) for outlier centers (P = .30). Conclusion. The present analysis is a novel risk-adjusted assessment of the performance of hospitals delivering pediatric surgical care. By identifying the existence of multiple poor performing outlier hospitals, this study provides valuable data for discussion as health care delivery systems continue to debate optimal resource distribution and regionalization of the surgical care of children.Surgery 06/2014; 156(2). DOI:10.1016/j.surg.2014.04.003 · 3.11 Impact Factor