Mitzi L. Dean

Changes in the Health Status of Women During and After Pregnancy

OBJECTIVE: To characterize the changes in health status experienced by a multi-ethnic cohort of women during and after pregnancy.DESIGN: Observational cohort.SETTING/PARTICIPANTS: Pregnant women from 1 of 6 sites in the San Francisco area (N=1,809).MEASUREMENTS AND MAIN RESULTS: Women who agreed to participate were asked to complete a series of telephone surveys that ascertained health status as well as demographic and medical factors. Substantial changes in health status occurred over the course of pregnancy. For example, physical function declined, from a mean score of 95.2 prior to pregnancy to 58.1 during the third trimester (0–100 scale, where 100 represents better health), and improved during the postpartum period (mean score, 90.7). The prevalence of depressive symptoms rose from 11.7% prior to pregnancy to 25.2% during the third trimester, and then declined to 14.2% during the postpartum period. Insufficient money for food or housing and lack of exercise were associated with poor health status before, during, and after pregnancy.CONCLUSIONS: Women experience substantial changes in health status during and after pregnancy. These data should guide the expectations of women, their health care providers, and public policy.

Variation in ICU Risk-Adjusted Mortality: Impact of Methods of Assessment and Potential Confounders

BACKGROUND Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. METHODS A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM(0) III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. RESULTS Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM(0) III (AUC, 0.809), and SAPS II (AUC, 0.873; p < 0.001). The models differed substantially in data abstraction times, as follows: MPM(0)III, 11.1 min (95% confidence interval [CI], 8.7 to 13.4); SAPS II, 19.6 min (95% CI, 17.0 to 22.2); and APACHE IV, 37.3 min (95% CI, 28.0 to 46.6). CONCLUSIONS We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM(0) III model offers a viable alternative without a substantial loss in accuracy.

Relationship Between Discharge Practices and Intensive Care Unit In-hospital Mortality Performance: Evidence of a Discharge Bias

Context:Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased. Objective:Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model. Design, Setting, and Participants:Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004. Measures:We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality—30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR. Results:In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of −0.021 (−0.040−0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of −0.049 (−0.142–0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance. Conclusions:Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.

Mortality Probability Model III and Simplified Acute Physiology Score II: Assessing Their Value in Predicting Length of Stay and Comparison to APACHE IV

BACKGROUND To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. CONCLUSIONS APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

Race/Ethnicity, Socioeconomic Status and the Health of Pregnant Women

We examined how traditional (income, education) and nontraditional (public assistance, material deprivation, subjective social standing) socioeconomic status (SES) indicators were associated with self-rated health, physical functioning, and depression in ethnically diverse pregnant women. Using multiple regression, we estimated the association of race/ethnicity (African American, Latino, Asian/Pacific Islander (PI) and white) and sets of SES measures on each health measure. Education, material deprivation, and subjective social standing were independently associated with all health measures. After adding all SES variables, race/ethnic disparities in depression remained for all minority groups; disparities in self-rated health remained for Asian/Pacific Islanders. Few race/ethnic differences were found in physical functioning. Our results contribute to a small literature on how SES might interact with race/ethnicity in explaining health.

Original ResearchCritical Care MedicineMortality Probability Model III and Simplified Acute Physiology Score II: Assessing Their Value in Predicting Length of Stay and Comparison to APACHE IV

BACKGROUND To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. CONCLUSIONS APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.

BACKGROUND Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times. OBJECTIVE To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment. MATERIALS AND METHODS We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx). RESULTS Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task. DISCUSSION Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures. CONCLUSIONS SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.

Revisit Rates and Associated Costs After an Emergency Department Encounter: A Multistate Analysis

Context Information is lacking about what happens to patients after discharge from the emergency department (ED). Contribution These researchers found that 1 of every 5 patients discharged from an ED had at least 1 revisit within 30 days. One third of revisits that took place within 3 days of the index ED visit were to a different ED, and the total cost of all revisits was more than the total cost of all initial visits. Caution The study was limited to 6 states, and only 1 state provided cost data. Implication Return visits are more frequent than previously recognized and may be more costly. Return visits to an emergency department (ED) or hospital after an index ED visit strain already overburdened EDs and the broader health care system (1, 2). These revisits may be planned follow-ups for progression of symptoms or disease, but they also may reflect failures of ambulatory follow-up (3, 4) or poor-quality care in the ED (58) or may be unrelated to the index ED visit. Concern over high costs, ED crowding, and waste of resources has led some organizations to consider prioritizing the reduction of preventable revisits (9, 10). But little is known at the population level about the rates at which ED patients return for care, whether revisit rates are higher for some diagnoses, or the costs associated with revisits. Without this information, creating effective and efficient interventions to reduce ED revisits with minimal unintended consequences will be difficult. Research on revisit rates has included studies that have examined revisits to a single institution (58, 11) or within a single state or insurance plan (12, 13) or have used data at multiple unaffiliated EDs, with returns to other EDs or admissions directly to inpatient care not captured (14). In addition, we are not aware of prior estimates of the costs associated with revisits. Therefore, the magnitude of the resource burden of revisits on the health care system is unknown. To address this, we examined acute care revisits after an index ED treat-and-discharge visit by using newly available multistate, longitudinal data that link encounters and allow identification of returns to any ED (not just the index ED) or admission to any acute care hospital after the index ED visit. We assessed the frequency of and costs associated with revisits within 30 days. Because most research has focused on 3-day revisit rates, we also calculated such rates overall and by whether the patient returned to or was admitted to the index or a different ED or hospital. Lastly, we examined the variation in 3-day revisit rates among the most common ED diagnoses and by state. Methods Data Sources Encounter data were obtained from the Healthcare Cost and Utilization Project (HCUP), which is maintained by the Agency for Healthcare Research and Quality. Data from 2006 to 2010 were abstracted for states for which the State Emergency Department Databases (SEDD) (15), State Inpatient Databases (SID) (16), and files linking these databases were available. The SEDD includes all ED treat-and-discharge visits and transfers to an ED (that is, all ED visits that did not result in an admission). The SID includes a variable identifying patients admitted from the ED. The discharge and visit records from these databases contain patient demographics; International Classification of Diseases, Ninth Revision, diagnoses; expected payer, admission, and discharge dates; and patient disposition. For some states, HCUP also provides linkage files (17) that enable identification of any subsequent ED visits or admissions to any ED or hospital within the state for an individual patient. Because our interest was in ED visits or admissions after an index ED visit, we included only states and years for which SEDD, SID, and linkage files were available with verified patient identifiers for more than 85% of index ED visits. This included Arizona, California, Florida, Nebraska, Utah, and Hawaii, with the following exceptions: We were able to include Arizona for 2006 and 2007 only because no revisit linkage files were available for the other years; data from 2010 for Hawaii and Utah were not available at the time of analysis and were excluded. Identifying Eligible Index Visits We included only index ED visits for adults who were discharged back to their home or place of residence. We excluded visits for pediatric (aged 17 years) patients. Assessment of Revisits To understand when revisits are most common and how they contribute to cost overall, we examined revisit rates and associated costs over the first 30 days after index ED visits. In addition, because most research has focused on 3-day revisit rates (5, 7, 14), we calculated revisit rates and costs for this period. The primary outcomes in this study were daily and cumulative revisit rates and costs over the first 30 days after an index ED treat-and-discharge visit and 3-day revisit rates and cumulative costs in the first 3 days after an index ED visit. If more than 1 revisit occurred in either the 3- or 30-day window, only the first revisit was included. We also examined revisit rates by place of revisit (to either the same or a different ED or hospital). We could not link index ED and subsequent ED visits or inpatient admissions by diagnosis for this analysis. Assessment of Cost Costs were estimated by assigning Medicare reimbursement rates in 2008 to Current Procedural Terminology codes for ED visits and diagnosis-related group codes for inpatient admissions. Florida was the only state with nearly complete (97%) capture of Current Procedural Terminology codes in SEDD; therefore, cost data are presented for Florida only. To reduce the effect of Floridas historical status as a high-utilization state (18), we express the costs as a percentage of index visits so that typical Florida utilization patterns are in both the numerator and the denominator. For calculating cumulative costs for the first 30 days after an index ED visit, we included only the first revisit. Costs were extrapolated for the other states by using the most common Current Procedural Terminology and diagnosis-related group codes from Florida for age and diagnosis combinations and by applying state-specific Medicare rates. These results are presented in the Appendix Table and Appendix Figures 1 and 2). Appendix Table. Costs in 6 States* of Revisits Within 3 Days, as a Percentage of Index ED Visit Costs, for the 10 Most Common Diagnoses Among Index ED Visits Appendix Figure 1. Cumulative revisit costs in Florida as a percentage of total index ED visit costs for revisits that ended with discharge versus revisits ending with admission. At day 30, costs for all revisits after which the patient was discharged home were 18% of total index ED visit costs. However, the costs for revisits that led to admission were 100% of total index ED visit costs. ED = emergency department. * Percentage of costs of all index ED visits. Appendix Figure 2. Cumulative revisit costs as a percentage of total index ED visit costs in 6 states. The 6 states include Arizona, California, Florida, Hawaii, Nebraska, and Utah. Revisit costs were 9% of total index ED visit costs at day 0, and revisit costs were at 100% of total index ED visit costs at day 16. At day 30, revisit costs were 145% of total index ED visit costs. ED = emergency department. * Percentage of costs of all index ED visits. Descriptive Variables Available patient characteristics included age, sex, race, primary insurance status, and diagnoses. Hospital variables included the number of beds, teaching and urban status, and type of ownership. Diagnosis categories were determined using Clinical Classification Software (19), which groups all International Classification of Diseases, Ninth Revision, diagnosis codes into clinically meaningful and mutually exclusive diagnosis categories. Statistical Analysis We calculated diagnosis-specific, 3-day revisit rates and 95% CIs. All rates are expressed as the total number of revisits within 3 days per 100 initial ED treat-and-discharge visits. Costs are reported as percentages of total index ED visit costs along with 95% CIs. To simultaneously account for the sampling weights and the clustering by hospital, we used survey-weighted analyses with hospital defined as the primary sampling unit (20, 21). Although most states had 5 years of data, there were a few exceptions (Arizona had 2 and Utah and Hawaii had 4 years of data). To produce approximate population-level estimates for the 5-year period in all of our analyses, we used a sampling weight equal to the maximum number of years (5 years) divided by the actual number of years in a particular state (that is, all observations were given a sampling weight of 1 except for those from Arizona, Utah, and Hawaii, which were given sampling weights of 2.5, 1.25, and 1.25, respectively). Of note, we used the surveylogistic procedure in SAS software (SAS Institute) for the risk-adjusted rates, surveyfreq for the diagnosis-specific revisit rates, and surveymeans with the ratio option for the cost analyses. To examine whether risk-adjusted revisit rates varied by state, we standardized the state rates using a logistic regression model that included patient-level factors of age, sex, insurance status, and Charlson comorbidity index (22). By including state as a fixed effect in these models, we were able to estimate predicted probabilities of revisit for each state and standardize to the mean values of covariates. These analyses were done by using the SAS procedure surveylogistic and the lsmeans command. To control for differences among states in the pattern of admission during initial ED visits, we included state-specific, risk-adjusted rates of admission during initial ED visits as a predictor when examining variation in risk-adjusted revisit rate by state. Analyses were conducted using SAS, version 9.3. Role of the Funding Source The Agency for Healthcare Research and Quality had no role

Thirty‐Day Hospital Readmissions in Systemic Lupus Erythematosus: Predictors and Hospital‐ and State‐Level Variation

Systemic lupus erythematosus (SLE) has one of the highest hospital readmission rates among chronic conditions. This study was undertaken to identify patient‐level, hospital‐level, and geographic predictors of 30‐day hospital readmissions associated with SLE.

Differences in mortality among patients with community-acquired pneumonia in California by ethnicity and hospital characteristics

PURPOSE To determine ethnic disparities in mortality for patients with community-acquired pneumonia, and the potential effects of hospital characteristics on disparities, we compared the risk-adjusted mortality of white, African American, Hispanic, and Asian American patients hospitalized for community-acquired pneumonia. METHODS We studied patients discharged with community-acquired pneumonia in 1996 from an acute care hospital in California (n = 54,874). Logistic regression models were used to examine the association between ethnicity and hospital characteristics and 30-day mortality after adjusting for clinical characteristics. RESULTS The overall 30-day mortality was 12.2%. After adjustment for demographic, clinical, and hospital characteristics, Hispanic (odds ratio [OR] = 0.81; 95% confidence interval [CI]: 0.73 to 0.90) and Asian American patients (OR = 0.88; 95% CI: 0.77 to 1.00) had lower mortality than did white patients, whereas African Americans had a similar mortality to whites (OR = 0.93; 95% CI: 0.83 to 1.06). There were no overall differences in mortality by hospital characteristics (i.e., teaching status, rural location, and public or district hospital). CONCLUSION Hispanics and Asian Americans have a lower risk of death from community-acquired pneumonia than whites in California. No overall differences in mortality were observed by hospital characteristics.