RESUMEN
BACKGROUND: Agreement regarding indications for vena cava filter (VCF) utilization in trauma patients has been in flux since the filter's introduction. As VCF technology and practice guidelines have evolved, the use of VCF in trauma patients has changed. This study examines variation in VCF placement among trauma centers. MATERIALS AND METHODS: A retrospective study was performed using data from the National Trauma Data Bank (2005-2014). Trauma centers were grouped according to whether they placed VCFs during the study period (VCF+/VCF-). A multivariable probit regression model was fit to predict the number of VCFs used among the VCF+ centers (the expected [E] number of VCF per center). The ratio of observed VCF placement (O) to expected VCFs (O:E) was computed and rank ordered to compare interfacility practice variation. RESULTS: In total, 65,482 VCFs were placed by 448 centers. Twenty centers (4.3%) placed no VCFs. The greatest predictors of VCF placement were deep vein thrombosis, spinal cord paralysis, and major procedure. The strongest negative predictor of VCF placement was admission during the year 2014. Among the VCF+ centers, O:E varied by nearly 500%. One hundred fifty centers had an O:E greater than one. One hundred sixty-nine centers had an O:E less than one. CONCLUSIONS: Substantial variation in practice is present in VCF placement. This variation cannot be explained only by the characteristics of the patients treated at these centers but could be also due to conflicting guidelines, changing evidence, decreasing reimbursement rates, or the culture of trauma centers.
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Utilización de Equipos y Suministros/estadística & datos numéricos , Pautas de la Práctica en Medicina/estadística & datos numéricos , Centros Traumatológicos/estadística & datos numéricos , Filtros de Vena Cava/estadística & datos numéricos , Heridas y Lesiones/terapia , Adolescente , Adulto , Bases de Datos Factuales/estadística & datos numéricos , Utilización de Equipos y Suministros/economía , Utilización de Equipos y Suministros/normas , Femenino , Humanos , Masculino , Persona de Mediana Edad , Guías de Práctica Clínica como Asunto , Pautas de la Práctica en Medicina/normas , Embolia Pulmonar/etiología , Embolia Pulmonar/prevención & control , Mecanismo de Reembolso/normas , Mecanismo de Reembolso/estadística & datos numéricos , Estudios Retrospectivos , Factores de Riesgo , Factores de Tiempo , Centros Traumatológicos/economía , Centros Traumatológicos/normas , Filtros de Vena Cava/economía , Trombosis de la Vena/etiología , Trombosis de la Vena/prevención & control , Heridas y Lesiones/complicaciones , Adulto JovenAsunto(s)
Traumatología/tendencias , Heridas y Lesiones/clasificación , Heridas y Lesiones/diagnóstico , Escala Resumida de Traumatismos , Historia del Siglo XX , Historia del Siglo XXI , Humanos , Puntaje de Gravedad del Traumatismo , Modelos Logísticos , Mejoramiento de la Calidad , Índices de Gravedad del Trauma , Traumatología/historiaRESUMEN
BACKGROUND: Outcome prediction models allow risk adjustment required for trauma research and the evaluation of outcomes. The advent of ICD-10-CM has rendered risk adjustment based on ICD-9-CM codes moot, but as yet no risk adjustment model based on ICD-10-CM codes has been described. METHODS: The National Trauma Data Bank provided data from 773,388 injured patients who presented to one of 747 trauma centers in 2016 with traumatic injuries ICD-10-CM codes and Injury Severity Score (ISS). We constructed an outcome prediction model using only ICD-10-CM acute injury codes and compared its performance with that of the ISS. RESULTS: Compared with ISS, the TMPM-ICD-10 discriminated survivors from non-survivors better (ROC TMPM-ICD-10 = 0.861 [0.860-0.872], ROC [reviever operating curve] ISS = 0.830 [0.823-0.836]), was better calibrated (HL [Hosmer-Lemeshow statistic] TMPM-ICD-10 = 49.01, HL ISS = 788.79), and had a lower Akaike information criteria (AIC TMPM-ICD10 = 30579.49; AIC ISS = 31802.18). CONCLUSIONS: Because TMPM-ICD10 provides better discrimination and calibration than the ISS and can be computed without recourse to Abbreviated Injury Scale coding, the TMPM-ICD10 should replace the ISS as the standard measure of overall injury severity for data coded in the ICD-10-CM lexicon. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level II.
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Clasificación Internacional de Enfermedades , Modelos Estadísticos , Medición de Riesgo , Heridas y Lesiones/mortalidad , Bases de Datos Factuales , Femenino , Humanos , Puntaje de Gravedad del Traumatismo , Masculino , Persona de Mediana Edad , Curva ROC , Reproducibilidad de los Resultados , Medición de Riesgo/métodos , Heridas y Lesiones/diagnósticoRESUMEN
INTRODUCTION: Readmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury. PATIENTS AND METHODS: We extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions. RESULTS: Of the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74). CONCLUSIONS: We found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.
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Hospitalización/estadística & datos numéricos , Medicare/economía , Alta del Paciente/estadística & datos numéricos , Readmisión del Paciente/estadística & datos numéricos , Heridas y Lesiones/terapia , Adulto , Toma de Decisiones en la Organización , Femenino , Encuestas de Atención de la Salud , Hospitales , Humanos , Modelos Logísticos , Masculino , Persona de Mediana Edad , Objetivos Organizacionales , Evaluación de Resultado en la Atención de Salud , Alta del Paciente/economía , Readmisión del Paciente/economía , Evaluación de Procesos, Atención de Salud , Calidad de la Atención de Salud , Estados Unidos , Heridas y Lesiones/economía , Heridas y Lesiones/epidemiologíaRESUMEN
The development of rapid point-of-care tests for HIV infection has greatly reduced the problem of failure to return for test results. Test manufacturers are now developing test kits that can test for two or even three diseases at the same time, multiple-disease test kits. This study reports on the sensitivity and specificity of HIV tests when included on multi-disease test kits. 1029 participants were recruited from 2011 to 2014. HIV test kit sensitivities ranged from 91.1 to 100%, and the HIV test kit specificities from 99.5 to 100%. The two HIV kits which used oral fluid instead of blood performed well.
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Infecciones por VIH/diagnóstico , Juego de Reactivos para Diagnóstico , Adulto , Femenino , Humanos , Masculino , Tamizaje Masivo/métodos , Persona de Mediana Edad , Pruebas en el Punto de Atención , Sensibilidad y Especificidad , Adulto JovenRESUMEN
Among substance abusers in the US, the discrepancy in the number who access substance abuse treatment and the number who need treatment is sizable. This results in a major public health problem of access to treatment. The purpose of this study was to examine characteristics of Persons Who Use Drugs (PWUDs) that either hinder or facilitate access to treatment. 2646 participants were administered the Risk Behavior Assessment (RBA) and the Barratt Impulsiveness Scale. The RBA included the dependent variable which was responses to the question "During the last year, have you ever tried, but been unable, to get into a drug treatment or detox program?" In multivariate analysis, factors associated with being unable to access treatment included: Previously been in drug treatment (OR=4.51), number of days taken amphetamines in the last 30days (OR=1.18), traded sex for drugs (OR=1.53), homeless (OR=1.73), Nonplanning subscale of the Barratt Impulsiveness Scale (OR=1.19), age at interview (OR=0.91), and sexual orientation, with bisexual men and women significantly more likely than heterosexuals to have tried but been unable to get into treatment. The answers to the question on "why were you unable to get into treatment" included: No room, waiting list; not enough money, did not qualify, got appointment but no follow through, still using drugs, and went to jail before program start. As expected, findings suggest that limiting organizational and financial obstacles to treatment may go a long way in increasing drug abuse treatment accessibility to individuals in need. Additionally, our study points to the importance of developing approaches for increasing personal planning skills/reducing Nonplanning impulsivity among PWUDs when they are in treatment as a key strategy to ensure access to additional substance abuse treatment in the future.
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Bisexualidad/estadística & datos numéricos , Accesibilidad a los Servicios de Salud/estadística & datos numéricos , Homosexualidad/estadística & datos numéricos , Personas con Mala Vivienda/estadística & datos numéricos , Conducta Impulsiva , Aceptación de la Atención de Salud/estadística & datos numéricos , Trastornos Relacionados con Sustancias/terapia , Adulto , Femenino , Humanos , Masculino , Persona de Mediana EdadRESUMEN
IMPORTANCE: The GCS was created forty years ago as a measure of impaired consciousness following head injury and thus the association of GCS with mortality in patients with traumatic brain injury (TBI) is expected. The association of GCS with mortality in patients without TBI (non-TBI) has been assumed to be similar. However, if this assumption is incorrect mortality prediction models incorporating GCS as a predictor will need to be revised. OBJECTIVE: To determine if the association of GCS with mortality is influenced by the presence of TBI. DESIGN/SETTING/PARTICIPANTS: Using the National Trauma Data Bank (2012; N=639,549) we categorized patients as isolated TBI (12.8%), isolated non-TBI (33%), both (4.8%), or neither (49.4%) based on the presence of AIS codes of severity 3 or greater. We compared the ability GCS to discriminate survivors from non-survivors in TBI and in non-TBI patients using logistic models. We also estimated the odds ratios of death for TBI and non-TBI patients at each value of GCS using linear combinations of coefficients. MAIN OUTCOME MEASURE: Death during hospital admission. RESULTS: As the sole predictor in a logistic model GCS discriminated survivors from non-survivors at an acceptable level (c-statistic=0.76), but discriminated better in the case of TBI patients (c-statistic=0.81) than non-TBI patients (c-statistic=0.70). In both unadjusted and covariate adjusted models TBI patients were about twice as likely to die as non-TBI patients with the same GCS for GCS values<8; for GCS values>8 TBI and non-TBI patients were at similar risk of dying. CONCLUSIONS: A depressed GCS predicts death better in TBI patients than non-TBI patients, likely because in non-TBI patients a depressed GCS may simply be the result of entirely reversible intoxication by alcohol or drugs; in TBI patients, by contrast, a depressed GCS is more ominous because it is likely due to a head injury with its attendant threat to survival. Accounting for this observation into trauma mortality datasets and models may improve the accuracy of outcome prediction.
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Traumatismos Craneocerebrales/diagnóstico , Traumatismos Craneocerebrales/mortalidad , Escala de Coma de Glasgow , Adulto , Anciano , Intoxicación Alcohólica/sangre , Bases de Datos Factuales , Servicio de Urgencia en Hospital , Etanol/sangre , Humanos , Modelos Logísticos , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Pronóstico , Reproducibilidad de los ResultadosRESUMEN
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .
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Biometría/métodos , Modelos Estadísticos , Simulación por Computador , Interpretación Estadística de Datos , Humanos , Modelos LinealesRESUMEN
IMPORTANCE: Massachusetts introduced health care reform (HCR) in 2006, expecting to expand health insurance coverage and improve outcomes. Because traumatic injury is a common acute condition with important health, disability, and economic consequences, examination of the effect of HCR on patients hospitalized following injury may help inform the national HCR debate. OBJECTIVE: To examine the effect of Massachusetts HCR on survival rates of injured patients. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 1,520,599 patients hospitalized following traumatic injury in Massachusetts or New York during the 10 years (2002-2011) surrounding Massachusetts HCR using data from the State Inpatient Databases. We assessed the effect of HCR on mortality rates using a difference-in-differences approach to control for temporal trends in mortality. INTERVENTION: Health care reform in Massachusetts in 2006. MAIN OUTCOME AND MEASURE: Survival until hospital discharge. RESULTS: During the 10-year study period, the rates of uninsured trauma patients in Massachusetts decreased steadily from 14.9% in 2002 to 5.0.% in 2011. In New York, the rates of uninsured trauma patients fell from 14.9% in 2002 to 10.5% in 2011. The risk-adjusted difference-in-difference assessment revealed a transient increase of 604 excess deaths (95% CI, 419-790) in Massachusetts in the 3 years following implementation of HCR. CONCLUSIONS AND RELEVANCE: Health care reform did not affect health insurance coverage for patients hospitalized following injury but was associated with a transient increase in adjusted mortality rates. Reducing mortality rates for acutely injured patients may require more comprehensive interventions than simply promoting health insurance coverage through legislation.
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Reforma de la Atención de Salud , Heridas y Lesiones/mortalidad , Adulto , Femenino , Humanos , Seguro de Salud/estadística & datos numéricos , Masculino , Massachusetts/epidemiología , Pacientes no Asegurados/estadística & datos numéricos , New York/epidemiología , Estudios Retrospectivos , Factores Socioeconómicos , Tasa de Supervivencia/tendencias , Heridas y Lesiones/economíaRESUMEN
BACKGROUND: Previous studies have reported that black race and lack of health insurance coverage are associated with increased mortality following traumatic injury. However, the association of race and insurance status with trauma outcomes has not been examined using contemporary, national, population-based data. METHODS: We used data from the National Inpatient Sample on 215,615 patients admitted to 1 of 836 hospitals following traumatic injury in 2010. We examined the effects of race and insurance coverage on mortality using two logistic regression models, one for patients younger than 65 years and the other for older patients. RESULTS: Unadjusted mortality was low for white (2.71%), black (2.54%), and Hispanic (2.03%) patients. We found no difference in adjusted survival for nonelderly black patients compared with white patients (adjusted odds ratio [AOR], 1.04; 95% confidence interval [CI], 0.90-1.19; p = 0.550). Elderly black patients had a 25% lower odds of mortality compared with elderly white patients (AOR, 0.75; 95% CI, 0.63-0.90; p = 0.002). After accounting for survivor bias, insurance coverage was not associated with improved survival in younger patients (AOR, 0.91; 95% CI, 0.77-1.07; p = 0.233). CONCLUSION: Black race is not associated with higher mortality following injury. Health insurance coverage is associated with lower mortality, but this may be the result of hospitals' inability to quickly obtain insurance coverage for uninsured patients who die early in their hospital stay. Increasing insurance coverage may not improve survival for patients hospitalized following injury. LEVEL OF EVIDENCE: Epidemiologic and prognostic study, level III.
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Cobertura del Seguro/economía , Pacientes no Asegurados/etnología , Grupos Raciales , Centros Traumatológicos/organización & administración , Heridas y Lesiones/terapia , Adulto , Anciano , Femenino , Estudios de Seguimiento , Disparidades en Atención de Salud/etnología , Humanos , Masculino , Persona de Mediana Edad , Pronóstico , Estudios Retrospectivos , Tasa de Supervivencia/tendencias , Estados Unidos/epidemiología , Heridas y Lesiones/economía , Heridas y Lesiones/etnologíaRESUMEN
CONTEXT: Several fracture prediction models that combine fractures at different sites into a composite outcome are in current use. However, to the extent individual fracture sites have differing risk factor profiles, model discrimination is impaired. OBJECTIVE: The objective of the study was to improve model discrimination by developing a 5-year composite fracture prediction model for fracture sites that display similar risk profiles. DESIGN: This was a prospective, observational cohort study. SETTING: The study was conducted at primary care practices in 10 countries. PATIENTS: Women aged 55 years or older participated in the study. INTERVENTION: Self-administered questionnaires collected data on patient characteristics, fracture risk factors, and previous fractures. MAIN OUTCOME MEASURE: The main outcome is time to first clinical fracture of hip, pelvis, upper leg, clavicle, or spine, each of which exhibits a strong association with advanced age. RESULTS: Of four composite fracture models considered, model discrimination (c index) is highest for an age-related fracture model (c index of 0.75, 47 066 women), and lowest for Fracture Risk Assessment Tool (FRAX) major fracture and a 10-site model (c indices of 0.67 and 0.65). The unadjusted increase in fracture risk for an additional 10 years of age ranges from 80% to 180% for the individual bones in the age-associated model. Five other fracture sites not considered for the age-associated model (upper arm/shoulder, rib, wrist, lower leg, and ankle) have age associations for an additional 10 years of age from a 10% decrease to a 60% increase. CONCLUSIONS: After examining results for 10 different bone fracture sites, advanced age appeared the single best possibility for uniting several different sites, resulting in an empirically based composite fracture risk model.
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Fracturas Óseas/diagnóstico , Fracturas Óseas/etiología , Modelos Estadísticos , Osteoporosis Posmenopáusica/diagnóstico , Factores de Edad , Anciano , Anciano de 80 o más Años , Estudios de Cohortes , Femenino , Fracturas Óseas/epidemiología , Humanos , Estudios Longitudinales , Persona de Mediana Edad , Osteoporosis Posmenopáusica/complicaciones , Osteoporosis Posmenopáusica/epidemiología , Pronóstico , Factores de RiesgoRESUMEN
Low body mass index (BMI) is a well-established risk factor for fracture in postmenopausal women. Height and obesity have also been associated with increased fracture risk at some sites. We investigated the relationships of weight, BMI, and height with incident clinical fracture in a practice-based cohort of postmenopausal women participating in the Global Longitudinal study of Osteoporosis in Women (GLOW). Data were collected at baseline and at 1, 2, and 3 years. For hip, spine, wrist, pelvis, rib, upper arm/shoulder, clavicle, ankle, lower leg, and upper leg fractures, we modeled the time to incident self-reported fracture over a 3-year period using the Cox proportional hazards model and fitted the best linear or nonlinear models containing height, weight, and BMI. Of 52,939 women, 3628 (6.9%) reported an incident clinical fracture during the 3-year follow-up period. Linear BMI showed a significant inverse association with hip, clinical spine, and wrist fractures: adjusted hazard ratios (HRs) (95% confidence intervals [CIs]) per increase of 5 kg/m(2) were 0.80 (0.71-0.90), 0.83 (0.76-0.92), and 0.88 (0.83-0.94), respectively (all p < 0.001). For ankle fractures, linear weight showed a significant positive association: adjusted HR per 5-kg increase 1.05 (1.02-1.07) (p < 0.001). For upper arm/shoulder and clavicle fractures, only linear height was significantly associated: adjusted HRs per 10-cm increase were 0.85 (0.75-0.97) (p = 0.02) and 0.73 (0.57-0.92) (p = 0.009), respectively. For pelvic and rib fractures, the best models were for nonlinear BMI or weight (p = 0.05 and 0.03, respectively), with inverse associations at low BMI/body weight and positive associations at high values. These data demonstrate that the relationships between fracture and weight, BMI, and height are site-specific. The different associations may be mediated, at least in part, by effects on bone mineral density, bone structure and geometry, and patterns of falling.
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Índice de Masa Corporal , Peso Corporal , Huesos , Fracturas Óseas , Modelos Biológicos , Posmenopausia/metabolismo , Factores de Edad , Anciano , Huesos/metabolismo , Huesos/patología , Femenino , Estudios de Seguimiento , Fracturas Óseas/epidemiología , Fracturas Óseas/metabolismo , Fracturas Óseas/fisiopatología , Humanos , Persona de Mediana Edad , Factores de RiesgoRESUMEN
BACKGROUND: Complications are common in the care of trauma patients and increase hospital length of stay (LoS). Because many factors influence LoS and because patients may experience more than a single complication, it is difficult to estimate the effect of individual complications on LoS. We describe here a mathematically principled approach to estimating the additional LoS caused by complications and provide estimates for additional LoS caused by 40 common complications. METHODS: The Pennsylvania Trauma Systems Foundation provided data on trauma patients hospitalized in one of 25 hospitals between 2002 and 2010. We estimated the attributable additional LoS in patients surviving to hospital discharge for 40 individual complications using a generalized linear model that controlled for anatomic injury and physiologic derangement at the time of admission, as well as patient factors (age, comorbidities, transfer status, type of insurance), discharge characteristics (day of week, destination), and year of admission. We also compared average risk-adjusted LoS among hospitals. RESULTS: Of 204,388 trauma patients surviving to discharge, 9.1% had one complication and 2.2% had multiple complications. Additional days LoS caused by individual complications ranged from less than 1 (bronchial intubation) to 16 days (wound dehiscence). Most complications added less than 1 week to LoS, but infectious complications added from 1 to 2 weeks; surgical complications added 2 to 3 weeks. If all complications could be eliminated, 24% of hospitalization days would be avoided. Individual hospitals' mean LoS differed from that predicted by our model by less than 1 day. CONCLUSION: Complications are common in the care of trauma patients and add moderately to LoS. Among all complications, surgical complications are associated with the greatest increases. LEVEL OF EVIDENCE: Prognostic study, level III.
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Tiempo de Internación/estadística & datos numéricos , Centros Traumatológicos/estadística & datos numéricos , Infección de Heridas/epidemiología , Heridas y Lesiones/terapia , Adulto , Femenino , Humanos , Incidencia , Masculino , Persona de Mediana Edad , Alta del Paciente/tendencias , Pennsylvania/epidemiología , Pronóstico , Estudios RetrospectivosRESUMEN
We examine goodness-of-fit tests for the proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. We derive a test statistic based on the Hosmer-Lemeshow test for binary logistic regression. Using a simulation study, we investigate the distribution and power properties of this test and compare these with those of three other goodness-of-fit tests. The new test has lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. Moreover, the test allows for the results to be summarized in a contingency table of observed and estimated frequencies, which is a useful supplementary tool to assess model fit. We illustrate the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents. The test proposed in this paper is similar to a recently developed goodness-of-fit test for multinomial logistic regression. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models.
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Interpretación Estadística de Datos , Modelos Logísticos , Adolescente , Simulación por Computador , Femenino , Humanos , Masculino , Trastornos Mentales/terapia , Oportunidad RelativaRESUMEN
The purposes of this study were to examine fracture risk profiles at specific bone sites, and to understand why model discrimination using clinical risk factors is generally better in hip fracture models than in models that combine hip with other bones. Using 3-year data from the GLOW study (54,229 women with more than 4400 total fractures), we present Cox regression model results for 10 individual fracture sites, for both any and first-time fracture, among women aged ≥55 years. Advanced age is the strongest risk factor in hip (hazard ratio [HR] = 2.3 per 10-year increase), pelvis (HR = 1.8), upper leg (HR = 1.8), and clavicle (HR = 1.7) models. Age has a weaker association with wrist (HR = 1.1), rib (HR = 1.2), lower leg (not statistically significant), and ankle (HR = 0.81) fractures. Greater weight is associated with reduced risk for hip, pelvis, spine, and wrist, but higher risk for first lower leg and ankle fractures. Prior fracture of the same bone, although significant in nine of 10 models, is most strongly associated with spine (HR = 6.6) and rib (HR = 4.8) fractures. Past falls are important in all but spine models. Model c indices are ≥0.71 for hip, pelvis, upper leg, spine, clavicle, and rib, but ≤0.66 for upper arm/shoulder, lower leg, wrist, and ankle fractures. The c index for combining hip, spine, upper arm, and wrist (major fracture) is 0.67. First-time fracture models have c indices ranging from 0.59 for wrist to 0.78 for hip and pelvis. The c index for first-time major fracture is 0.63. In conclusion, substantial differences in risk profiles exist among the 10 bones considered.
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Fracturas Óseas/complicaciones , Fracturas Óseas/patología , Internacionalidad , Osteoporosis/complicaciones , Osteoporosis/epidemiología , Intervalos de Confianza , Femenino , Humanos , Estudios Longitudinales , Persona de Mediana Edad , Análisis Multivariante , Modelos de Riesgos Proporcionales , Factores de RiesgoRESUMEN
CONTEXT: Complications are common in the care of trauma patients and contribute to morbidity, mortality, and cost. However, no comprehensive list of surgical complications is widely accepted. OBJECTIVES: To create an empirical list of complications based on the International Classification of Diseases, Ninth Revision (ICD-9) lexicon and estimate the contribution of these complications to mortality. DESIGN: Retrospective database analysis. SETTING: Office of Statewide Health Planning and Development data set. PATIENTS: The Office of Statewide Health Planning and Development provided information on 409,393 patients admitted to 1 of 159 California hospitals between 2004 and 2008. We defined a complication to be any ICD-9- coded condition that accrued after hospital admission and significantly increased mortality. MAIN OUTCOME MEASURES: Odds of mortality for individual complications and number of excess deaths due to individual complications based on attributable risk fractions. RESULTS: Eighty-two different ICD-9 codes contributed significantly to mortality as complications. Odds ratios ranged from 1.02 (hyperosmolarity) to 46.1 (ventricular fibrillation). There were a total of 175,299 complications (range, 0-14; average 0.4/patient). Twenty-four percent of patients had at least 1 complication. Mortality increased with the number of complications; each additional complication increased mortality by 8%. Absent any complications, there would have been 7292 fewer deaths, a 64% reduction in overall mortality. CONCLUSIONS: Most complication-related mortality is due to 25 individual complications. Eliminating all complications might prevent two-thirds of deaths, but because many complications are not preventable, this figure is the upper bound on possible mortality reduction. Hospitals vary in their proportions of deaths due to complications, and thus, efforts to prevent complications might improve survival at some hospitals.
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Heridas y Lesiones/complicaciones , Heridas y Lesiones/mortalidad , Adolescente , Adulto , Femenino , Mortalidad Hospitalaria , Humanos , Masculino , Persona de Mediana Edad , Embolia Pulmonar/mortalidad , Estudios Retrospectivos , Ajuste de Riesgo , Adulto JovenRESUMEN
BACKGROUND: Severity-adjusted mortality is an unequivocal measure of burn care success. Hospitals can be compared on this metric using administrative data because information required for calculating statistically adjusted risk of mortality is routinely collected on hospital admission. METHODS: The New York State Department of Health provided information on all 13,113 thermally injured patients hospitalized at 1 of 194 hospitals between 2004 and 2008. We compared hospital survival rates using a random effects logistic model of mortality that incorporated age and several predictors that were present on admission and captured as International Classification of Diseases-9 codes: burn surface area, inhalation injury, three measures of physiologic compromise, and four medical comorbidities. Hospitals were compared on the adjusted odds of death and the number of excess deaths. RESULTS: Overall mortality was 3.2%. Nine high-volume hospitals (>100 patients/year) cared for 83% of patients with burn injuries. Overall variability of the odds of mortality among these high-volume centers was modest (median odds ratio=1.2) and we found little evidence for differences in the adjusted odds of mortality. A secondary analysis of the 185 low-volume hospitals that cared for 2,235 patients disclosed only 24 deaths. When examined in aggregate, these hospitals had better than predicted risk-adjusted mortality; a logical explanation is judicious case selection. CONCLUSIONS: Administrative hospital discharge data are extensive and comparably enough collected to allow comparison of the performance of burn centers. Risk-adjusted models show that patients have statistically indistinguishable risk-adjusted odds of mortality regardless of which hospital in New York State cared for them.