RESUMEN
OBJECTIVE: Screening for blunt intra-abdominal injury in children often includes directed laboratory evaluation that guides need for computed tomography. We sought to evaluate the use of urinalysis in identifying patients with clinically important intraabdominal injury ( ci -IAI). METHODS: A retrospective chart review was performed for all patients less than 18 years who presented with blunt mechanisms at a level I trauma center between 2016 and 2019. Exclusion criteria included transfer from an outside facility, physical abuse, and death within thirty minutes of arrival. Demographics, physical exam findings, serum chemistries, urinalysis, and imaging were reviewed. Clinically important intraabdominal injury was defined as injury requiring ≥2 nights admission, blood transfusion, angiography with embolization, or therapeutic surgery. RESULTS: Two hundred forty patients were identified. One hundred sixty-five had a completed urinalysis. For all patients an abnormal chemistry panel and abnormal physical exam had a sensitivity of 88.9% and a negative predictive value of 99.3%. Nine patients had a ci -IAI. Patients with a ci -IAI were more likely to have abdominal pain, tenderness on exam, and elevated hepatic enzymes. When patients were stratified by the presence of an abnormal chemistry or physical exam with or without microscopic hematuria, urinalysis did not improve the ability to identify patients with a ci -IAI. In fact, presence of microscopic hematuria increased the rate of false positives by 12%. CONCLUSIONS: Microscopic hematuria was not a useful marker for ci -IAI and may lead to falsely assuming a more serious injury.
Asunto(s)
Traumatismos Abdominales , Hematuria , Urinálisis , Heridas no Penetrantes , Humanos , Masculino , Niño , Hematuria/etiología , Estudios Retrospectivos , Femenino , Traumatismos Abdominales/complicaciones , Traumatismos Abdominales/diagnóstico , Heridas no Penetrantes/complicaciones , Adolescente , Preescolar , Centros Traumatológicos , Tomografía Computarizada por Rayos X , Sensibilidad y Especificidad , Lactante , Valor Predictivo de las PruebasRESUMEN
BACKGROUND: Physical abuse is a major public health concern and a leading cause of morbidity and mortality in infants. Clinical decision tools derived from trauma registries can facilitate timely risk-stratification. The Trauma Quality Improvement Program (TQIP) database does not report age for children <1 year who are at highest risk for abuse. We report a method to capture these infants despite the missing age. METHODS: Patients ≤17 years were identified from TQIP (2017-2019). The primary outcomes included injuries resulting from confirmed or suspected child abuse captured by diagnosis codes or report/investigation of physical abuse, or different caregiver at discharge available in TQIP. We used two methods to select infants within TQIP. In the first, World Health Organization (WHO) growth standards for stature or length-for-age and weight-for-age were selected to capture children younger than 1 year. In the second, a K-means machine learning algorithm was used to cluster patients by weight and height. We compared outcome and injury data with and without patients <1 year. RESULTS: Using the WHO growth standard 19,916 children <1 year were identified. A total of 20,513 patients had a report of physical abuse filed, and 9393 were infants <1 year. Increased age-adjusted odds ratios [95% CI] were seen for fractures of the upper limb (1.28 [1.22-1.34]), vertebrae (1.89 [1.68-2.13]), ribs (5.2 [4.8-5.63]), and spinal cord (3.39 [2.85-4.02]) and head injuries (1.55 [1.5-1.6]) with infants included. CONCLUSIONS: In a nationwide trauma registry, WHO growth standards can be used to capture patients under one year who are more adversely impacted by maltreatment. TYPE OF STUDY: Retrospective, Cross-sectional. LEVEL OF EVIDENCE: Level III, Diagnostic.
Asunto(s)
Maltrato a los Niños , Sistema de Registros , Heridas y Lesiones , Humanos , Lactante , Maltrato a los Niños/diagnóstico , Maltrato a los Niños/estadística & datos numéricos , Masculino , Femenino , Preescolar , Heridas y Lesiones/epidemiología , Heridas y Lesiones/etiología , Mejoramiento de la Calidad , Medición de Riesgo/métodos , Recién Nacido , Niño , Estudios Retrospectivos , Aprendizaje Automático , AdolescenteRESUMEN
BACKGROUND: Venous thromboembolism (VTE) causes significant morbidity in pediatric trauma patients. We applied machine learning algorithms to the Trauma Quality Improvement Program (TQIP) database to develop and validate a risk prediction model for VTE in injured children. METHODS: Patients ≤18 years were identified from TQIP (2017-2019, n = 383,814). Those administered VTE prophylaxis ≤24 h and missing the outcome (VTE) were removed (n = 347,576). Feature selection identified 15 predictors: intubation, need for supplemental oxygen, spinal injury, pelvic fractures, multiple long bone fractures, major surgery (neurosurgery, thoracic, orthopedic, vascular), age, transfusion requirement, intracranial pressure monitor or external ventricular drain placement, and low Glasgow Coma Scale score. Data was split into training (n = 251,409) and testing (n = 118,175) subsets. Machine learning algorithms were trained, tested, and compared. RESULTS: Low-risk prediction: For the testing subset, all models outperformed the baseline rate of VTE (0.15%) with a predicted rate of 0.01-0.02% (p < 2.2e-16). 88.4-89.4% of patients were classified as low risk by the models. HIGH-RISK PREDICTION: All models outperformed baseline with a predicted rate of VTE ranging from 1.13 to 1.32% (p < 2.2e-16). The performance of the 3 models was not significantly different. CONCLUSION: We developed a predictive model that differentiates injured children for development of VTE with high discrimination and can guide prophylaxis use. LEVEL OF EVIDENCE: Prognostic, Level II. TYPE OF STUDY: Retrospective, Cross-sectional.
Asunto(s)
Tromboembolia Venosa , Humanos , Niño , Tromboembolia Venosa/epidemiología , Tromboembolia Venosa/etiología , Tromboembolia Venosa/prevención & control , Estudios Retrospectivos , Estudios Transversales , Factores de Riesgo , Algoritmos , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Obesity is a growing public health concern that places patients at risk of morbidity and mortality following surgery. We sought to determine whether obesity influences our resource utilization and postoperative outcomes for patients who present with appendicitis. METHODS: Charts were reviewed for patients age 1-18 years identified from a prospective registry who presented with a diagnosis of appendicitis from 2017 to 2020. Patients who underwent appendectomy were eligible. Charts were reviewed for demographics, imaging studies, laboratory studies, length of stay, operative times and thirty-day postoperative adverse events defined as return to the emergency room, re-admission, postoperative abscess or return to the operating room. A multivariate logistic regression analysis was performed to identify differences in resource utilization and outcome. RESULTS: A total of 451 patients were identified. There were 126 obese patients (27.9%). Obese patients were not more likely to present with perforated appendicitis and were not more likely to undergo computed tomography scans. All patients underwent laparoscopic appendectomy. Although intraoperative times were significantly longer for Black patients and older patients, BMI did not influence length of surgery. Length of stay was significantly higher for younger patients (p = 0.019). Adverse events were seen in 38 patients (8.4%). There was no association between BMI and adverse events. CONCLUSIONS: Within our standardized management pathway, obesity does not influence management or patient outcomes for the treatment of appendicitis. Furthermore, obese patients did not require additional resource utilization. LEVEL OF EVIDENCE: III.