Epidemiology of neonatal respiratory failure in the United States: projections from California and New York.

We wanted to determine the incidence, cost, outcome, and patterns of care for neonates requiring mechanical ventilation (MV) in the United States. Using 1994 state hospital discharge data from California and New York, we conducted an observational study of all neonatal hospitalizations (n = 16,405) with MV, comparing outcomes at centers of different technological capability, and generating national projections using census and natality reports. The MV rate was 18 per 1,000 live births. Although the incidence was much higher in lower birth weight (BW) babies, one-third had normal BW. The incidence was higher in boys (20 versus 15.6 per 1,000) and in blacks (29 per 1,000). Hospital mortality was 11.1%, higher in minority groups, and associated with low BW, congenital anomalies, and major hemorrhage. Mean hospital length of stay and costs were 31.1 d and $51,700. Half of all deaths occurred at lower level centers. There are 80,000 cases per year in the United States with 8,500 deaths and total hospital costs of $4.4 billion. We conclude neonatal respiratory failure is common, expensive, and frequently fatal. There are a surprisingly large number of normal BW cases and there are large racial differences.

Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.

OBJECTIVE: To determine the incidence, cost, and outcome of severe sepsis in the United States. DESIGN: Observational cohort study. SETTING: All nonfederal hospitals (n = 847) in seven U.S. states. PATIENTS: All patients (n = 192,980) meeting criteria for severe sepsis based on the International Classification of Diseases, Ninth Revision, Clinical Modification. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We linked all 1995 state hospital discharge records (n = 6,621,559) from seven large states with population and hospital data from the U.S. Census, the Centers for Disease Control, the Health Care Financing Administration, and the American Hospital Association. We defined severe sepsis as documented infection and acute organ dysfunction using criteria based on the International Classification of Diseases, Ninth Revision, Clinical Modification. We validated these criteria against prospective clinical and physiologic criteria in a subset of five hospitals. We generated national age- and gender-adjusted estimates of incidence, cost, and outcome. We identified 192,980 cases, yielding national estimates of 751,000 cases (3.0 cases per 1,000 population and 2.26 cases per 100 hospital discharges), of whom 383,000 (51.1%) received intensive care and an additional 130,000 (17.3%) were ventilated in an intermediate care unit or cared for in a coronary care unit. Incidence increased >100-fold with age (0.2/1,000 in children to 26.2/1,000 in those >85 yrs old). Mortality was 28.6%, or 215,000 deaths nationally, and also increased with age, from 10% in children to 38.4% in those >85 yrs old. Women had lower age-specific incidence and mortality, but the difference in mortality was explained by differences in underlying disease and the site of infection. The average costs per case were $22,100, with annual total costs of $16.7 billion nationally. Costs were higher in infants, nonsurvivors, intensive care unit patients, surgical patients, and patients with more organ failure. The incidence was projected to increase by 1.5% per annum. CONCLUSIONS: Severe sepsis is a common, expensive, and frequently fatal condition, with as many deaths annually as those from acute myocardial infarction. It is especially common in the elderly and is likely to increase substantially as the U.S. population ages.

Quality-adjusted survival in the first year after the acute respiratory distress syndrome.

There is little information on long-term outcome after acute respiratory distress syndrome (ARDS). We measured quality-adjusted survival in the first year after ARDS in a prospective cohort (n = 200). All patients met traditional criteria for ARDS. Patients with sepsis and acute nonpulmonary organ dysfunction at presentation were excluded. The cohort was healthy before onset of ARDS as evidenced by high functional status (mean Karnofsky Performance Status index: 82.2/100 where >/= 80 = able to perform normal activities independently) and minimal comorbid illness (mean Charlson-Deyo comorbidity score: 0.32/17 where 0 = absence of chronic illness). We determined quality-adjusted life-years (QALYs) using the Quality of Well-being (QWB) scale (0 to 1 scale where 1 = optimal well-being), measured at 6 and 12 mo. Survival was 69.5 +/- 5.0% at 1 month, fell to 55.7 +/- 3.7% at 6 mo, and did not change at 12 mo, yielding a survival of 59 life-years in the first year per 100 patients with ARDS. QWB was low at 6 and 12 mo (0.59 +/- 0.015 and 0.60 +/- 0.015), yielding a quality-adjusted survival of 36 QALYs per 100 patients (sensitivity range: 21 to 46 QALYs). We conclude that ARDS developing in previously healthy patients is associated with poor quality-adjusted survival. These data are important for cost-effectiveness analyses and long-term care.

Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models.

OBJECTIVE: Logistic regression (LR), commonly used for hospital mortality prediction, has limitations. Artificial neural networks (ANNs) have been proposed as an alternative. We compared the performance of these approaches by using stepwise reductions in sample size. DESIGN: Prospective cohort study. SETTING: Seven intensive care units (ICU) at one tertiary care center. PATIENTS: Patients were 1,647 ICU admissions for whom first-day Acute Physiology and Chronic Health Evaluation III variables were collected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We constructed LR and ANN models on a random set of 1,200 admissions (development set) and used the remaining 447 as the validation set. We repeated model construction on progressively smaller development sets (800, 400, and 200 admissions) and retested on the original validation set (n = 447). For each development set, we constructed models from two LR and two ANN architectures, organizing the independent variables differently. With the 1,200-admission development set, all models had good fit and discrimination on the validation set, where fit was assessed by the Hosmer-Lemeshow C statistic (range, 10.6-15.3; p > or = .05) and standardized mortality ratio (SMR) (range, 0.93 [95% confidence interval, 0.79-1.15] to 1.09 [95% confidence interval, 0.89-1.38]), and discrimination was assessed by the area under the receiver operating characteristic curve (range, 0.80-0.84). As development set sample size decreased, model performance on the validation set deteriorated rapidly, although the ANNs retained marginally better fit at 800 (best C statistic was 26.3 [p = .0009] and 13.1 [p = .11] for the LR and ANN models). Below 800, fit was poor with both approaches, with high C statistics (ranging from 22.8 [p <.004] to 633 [p <.0001]) and highly biased SMRs (seven of the eight models below 800 had SMRs of <0.85, with an upper confidence interval of <1). Discrimination ranged from 0.74 to 0.84 below 800. CONCLUSIONS: When sample size is adequate, LR and ANN models have similar performance. However, development sets of < or = 800 were generally inadequate. This is concerning, given typical sample sizes used for individual ICU mortality prediction.

Short-term and long-term outcome prediction with the Acute Physiology and Chronic Health Evaluation II system after orthotopic liver transplantation.

OBJECTIVE: To evaluate the relationship between the postoperative Acute Physiology and Chronic Health Evaluation (APACHE) II score and mortality at hospital discharge and at 1 yr in liver transplant recipients. POPULATION: Adult orthotopic liver transplant (OLTX) recipients (n = 599) admitted to the intensive care unit postoperatively at a university hospital. METHODS: The cohort was split randomly into development and validation sets. Three models were compared for each end point: a) the original APACHE II slope with the original APACHE II postgastrointestinal surgery intercept; b) the original APACHE II slope with an OLTX-specific intercept generated from the development set; and c) an OLTX-specific slope and intercept generated from the development set. Goodness-of-fit and calibration were assessed by the Hosmer-Lemeshow C statistic (where p>.05 suggests good fit) and standardized mortality ratios. Discrimination was assessed by receiver operator characteristic area under the curve analysis. MEASUREMENTS AND MAIN RESULTS: Hospital and 1-yr mortality rates were 9.9% and 15.9%, respectively. The APACHE II score was strongly associated with mortality (chi-square, p<.0001), but when used with the original equation, it significantly overestimated hospital mortality (standardized mortality ratio, 0.73 [confidence interval, 0.58-0.99]). Using the OLTX-specific approaches, goodness-of-fit for both hospital and 1-yr mortality was good (p = .2-.57) but discrimination was only moderate (receiver operator characteristic area under the curve, 0.675-0.723). CONCLUSIONS: APACHE II is a good predictor of short- and long-term mortality after liver transplantation, especially when using OLTX-specific coefficients. Because fit and calibration were better than discrimination, APACHE II will be most useful in the prediction of risk for groups of patients (e.g., in clinical trials or institutional comparisons) rather than for individuals. This study raises the possibility that APACHE II may be useful for long-term mortality prediction in other critically ill populations. The overestimation of mortality using the original equation suggests that orthotopic liver transplantation, by reversing the underlying pathophysiology, may modify risk.

Measuring resource use in the ICU with computerized therapeutic intervention scoring system-based data.

BACKGROUND AND OBJECTIVE: In this era of health-care reform, there is increasing need to monitor and control health-care resource consumption. This requires the development of measurement tools that are practical, uniform, reproducible, and of sufficient detail to allow comparison among institutions, among select groups of patients, and among individual patients. We explored the feasibility of generating an index of resource use based on the Therapeutic Intervention Scoring System (TISS) from hospital electronic billing data. Such an index is potentially comparable across institutions, allows assessment of care at many levels, is well understood by clinicians, and captures many of the resources relevant to the ICU. DESIGN: We developed an automated mapping of the hospital billing database into the different items of TISS and generated computerized active TISS scores on 1,372 ICU days. The computerized score was then validated by comparison to prospectively gathered active TISS scores by trained data collectors. SETTING: Eight ICUs within a university teaching institution. PATIENTS: We studied 1,229 general medical and surgical ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Active TISS scores ranged from 0 to 31 points. The two scores were well correlated (R2=0.53) and highly calibrated (as assessed by regression of active TISS on mean computerized active TISS [R2=0.85]). The scores were identical on 756 days (55.6%) and differed by < or = 3 TISS points on an additional 387 (28.2%) days. Interreliability assessment suggested substantial agreement (kappa statistic=0.71). The discriminatory power of the computerized score to identify different levels of ICU resource use was excellent as assessed by area under the receiver operating characteristics curves at four threshold points (0.91, 0.87, 0.89, and 0.88). Performance of the computerized score was similar across medical, coronary, and surgical ICU patient groups. CONCLUSION: An automated algorithm can reproduce valid TISS scores from standard hospital billing data, allowing comparison of patients and groups of patients in order to better understand ICU resource use.

The effect of managed care on ICU length of stay: implications for medicare.

OBJECTIVE: To determine whether insurance status (managed care vs traditional commercial and Medicare) influences resource consumption (as measured by length of stay [LOS]) in the intensive care unit (ICU). DESIGN: Retrospective analysis of the 1992 Massachusetts state hospital discharge database, using prospectively developed and validated risk-stratification models. SETTING: All nonfederal hospitals in Massachusetts. SUBJECTS: Of all adult hospitalizations where an ICU stay was incurred (n=104270), we selected those covered by 1 of 4 payer groups (n=88050): (1) commercial fee-for-service (patients aged <65 years); (2) commercial managed care (patients aged <65 years); (3) traditional Medicare (patients aged >/=65 years); and (4) Medicare-sponsored managed care (patients aged >/=65 years). MAIN OUTCOME MEASURE: Mean ICU LOS. ANALYSIS: The ICU LOS regression models were constructed using split-halves validation to adjust for differences in age, sex, severity of illness, diagnosis, discharge status, and payer. Separate models were constructed for those younger than 65 years and those aged 65 years or older. Robustness of the models was explored using goodness of fit and correlation. The effect of payer on hospital mortality was also explored using logistic regression. Observed minus predicted mean ICU LOS and mortality rates were correlated with managed care penetration at the hospital level. RESULTS: The ICU LOS models performed well (R2=0.84 and R2L [likelihood ratio statistic]=0.92 for the development set, and R2=0.83 and R2L=0.89 for the validation set). Significant covariables affecting LOS included age, severity of principal illness, comorbidity, reason for admission, and discharge status (P<.001 for each). Among the cohort younger than 65 years (n=27805), although unadjusted mean ICU LOS was shorter (2.9 vs 3.43 days; P<.05) for those covered by managed care organizations, payer status had no independent effect on ICU LOS (P=.48). Among those older than 65 years, there was neither a difference in unadjusted ICU LOS (3.94 vs 3.88 days; P>/=.05) nor an independent effect of payer on ICU LOS (P=.35). Unadjusted mortality was lower among managed care patients (3.9% vs 5.1% in patients aged <65 years [P<.05] and 8.7% vs 12.1% in patients aged > or = 65 years [P<.05]). Age, severity of principal diagnosis, comorbidity, and reason for admission significantly influenced mortality (P<.001). After controlling for these factors with the mortality model (R2L=0.92 and 0.89, C statistic [12 df]=8.45 and 17.58, and P=.75 and .13 [where a large P reflects good agreement] for the development and validation sets, respectively), payer continued to have a small but significant effect on mortality (odds ratios ranging from 1.67 at 0.1% probability of death to 1.11 at 30% probability of death.) Managed care penetration among the commercially insured varied across hospitals (n=82) from 0% to 68%. There was no correlation between managed care penetration and either ICU LOS (R2=0.04; P=.09) or mortality (R2=0.0; P=.88). CONCLUSIONS: Though patients covered under managed care consume fewer ICU resources, this appears to be primarily attributable to a difference in patient-related factors. Thus, as managed care case mix changes in the future to include sicker and older patients, the initial advantages of reduced resource consumption may diminish.

Death rates among patients hospitalized with community-acquired pneumonia: a reexamination with data from three states.

OBJECTIVES: Death rates for community-acquired pneumonia based on relatively small-scale, published studies tend to exceed 15% to 20%. This study reexamined these estimates by using very large, population-based databases. METHODS: Death rates from 1993 associated with community-acquired pneumonia were reexamined with hospital discharge data from all of Washington, Illinois, and Florida. RESULTS: These death rates were substantially lower (7.0%, 8.1%, and 9.7%, respectively) than what appears in the literature. Significant risk factors for dying were being 65 years of age or older (odds ratio [OR] = 2.9), being positive for human immunodeficiency virus (OR = 2.9), and having a high severity of illness (OR = 7.1). CONCLUSION: Sampling bias associated with selection for hospital admissions explain the discrepancy between previous and this study's results.