FACTORS THAT DETERMINE PATIENT outcome after

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1 1108 ORIGINAL ARTICLE Measuring Medical Complexity During Inpatient Rehabilitation After Traumatic Brain Injury David K. Ryser, MD, Marlene J. Egger, PhD, Susan D. Horn, PhD, Diana Handrahan, BS, Partha Gandhi, PhD, Erin D. Bigler, PhD ABSTRACT. Ryser DK, Egger MJ, Horn SD, Handrahan D, Gandhi P, Bigler ED. Measuring medical complexity during inpatient rehabilitation after traumatic brain injury. Arch Phys Med Rehabil 2005;86: Objective: To compare the performance of 3 severity of illness (SOI) indices the Comprehensive Severity Index (CSI), All Patient Refined Diagnosis Related Groups Severity of Illness, case-mix group (CMG) and 5 well-known neurologic parameters, as measures of medical complexity. Design: Retrospective chart review. Setting: Inpatient rehabilitation center within a level I trauma center. Participants: Consecutive traumatic brain injury (TBI) admissions (N 212). Intervention: Acute inpatient TBI rehabilitation. CSI and neurologic parameters were scored by chart extraction. SOI was based on diagnosis codes by using 3M PC Grouper software, version 15. Main Outcome Measures: Adjusted R 2 was used to predict rehabilitation charges as a proxy of medical complexity. Results: The highest adjusted R 2 values for single variables predicting charges were: CMG.349, CSI.293, duration of posttraumatic amnesia.260. Adjusted R 2 values for the CMG combined with the CSI, 5 neurologic parameters, and SOI to predict charges were.446,.431, and.365, respectively. Conclusions: The CMG was the best single predictor of rehabilitation charges for TBI. Predictive ability was better when the CMG was combined with the CSI or a combination of the 5 neurologic parameters. A severity index based on objective clinical findings rather than diagnostic codes may have distinct advantages for rehabilitation outcome studies and reimbursement methodology. Key Words: Brain injuries; Diagnosis-related groups; Rehabilitation; Severity of illness index by American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation From the Division of PM&R (Ryser) and Statistical Data Center (Egger, Handrahan), LDS Hospital, Salt Lake City, UT; Public Health Programs, University of Utah, Lake City, UT (Egger); International Severity Information Systems, Salt Lake City, UT (Horn); and Departments of Psychology and Neuroscience, Brigham Young University, Provo, UT (Gandhi, Bigler). Supported by the Deseret Foundation and the Urban Central Region of Intermountain Health Care and the Department of Psychology, Brigham Young University. A commercial party having direct financial interest in the results of the research supporting this article has conferred or will confer a financial benefit upon the author or 1 or more of the authors. Horn is a full-time employee and principal stockholder of International Severity Information Systems Inc, owner of the Comprehensive Severity Index system. Reprint requests to David K. Ryser, MD, Neuro Specialty Rehabilitation Unit, LDS Hospital, 8th Ave and C St, Salt Lake City, UT 84143, lddryser@ihc.com /05/ $30.00/0 doi: /j.apmr FACTORS THAT DETERMINE PATIENT outcome after inpatient rehabilitation for traumatic brain injury (TBI) are numerous 1 and are similar to those that influence hospital resource use during acute inpatient rehabilitation (table 1). 2,3 The patient s brain injury, premorbid characteristics, and comorbid conditions related to traumatic injury or subsequent management, constitute the medical complexity of that patient. Accounting for differences in medical complexity between cohorts of patients is known as risk adjustment. According to Iezzoni, The goal of risk adjustment is to account for pertinent patient characteristics before making inferences about the effectiveness or quality of care based on patient outcomes. 4(p3) There is a growing body of prospective and retrospective data describing the functional outcome of TBI patients at the time of discharge from inpatient rehabilitation. 5-8 The medical complexity of these patients must be accounted for to understand their outcomes or to compare one cohort with another. The introduction of the prospective payment system (PPS) for inpatient rehabilitation facilities has increased interest in methods that measure medical complexity because of its well-known effect on hospital resource use. 9 Stineman s research on function-related groups (FRGs) documents much of the development of the PPS functional classification for medical rehabilitation. Her work, which was based on large numbers of discharges in the Uniform Data System (UDS) database, showed that FRGs accounted for 31.5% of the variance in the natural log of length of stay (LOS) for inpatient rehabilitation. 19 The federal government adopted the FRG system that, with minimal modifications, became the case-mix groups (CMGs) within the PPS. TBI is 1 of the 21 rehabilitation impairment categories in the PPS. TBI patients are classified into 5 CMGs, based on the cognitive and motor subtotals of the FIM instrument, at admission to rehabilitation. It has been shown that the complexity of secondary diagnoses within each FRG is also related to rehabilitation LOS and costs. 9,20 In response to evidence that comorbidities are a significant cause of variation in resource use beyond what admission functional status explained, PPS contains a 4-tiered payment adjustment for comorbidities that uses selected International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes of secondary diagnoses. 20 Unfortunately, the research basis of this methodology is not published in the medical literature. Its limitations include reliance on ICD-9-CM diagnostic coding and allowance for payment adjustment for only 1 relevant comorbidity per admission. Stineman et al 19 showed that the addition of ICD-9-CM comorbidity diagnostic information increased the variance in rehabilitation LOS explained by FRGs by only 1.9%, as compared with the 31.5% variance explained by FRGs alone, as measured by a cross-validation R 2. An improved measure of medical complexity would be useful for this reimbursement methodology, as well as for outcomes studies for TBI and other rehabilitation impairment categories. Several instruments for measuring medical severity or risk adjustment have been developed. 21 Medicare s adoption of

2 MEASURING MEDICAL COMPLEXITY, Ryser 1109 Table 1: Determinants of Hospital Resource Use During Inpatient Rehabilitation for TBI as Measured by 4 Severity Indices and a Combination of Neurologic Parameters Determinants SOI CSI CMG PPS Neuro Premorbid personal characteristics Age and sex Personality and relationships Cultural and socioeconomic Premorbid health and functional ability / / Brain injury Principal diagnosis / Physical examination findings Radiographic findings Laboratory data Comorbidities and complications Secondary diagnoses Physical exam findings Radiographic findings Laboratory data Functional ability during rehabilitation (a consequence of all of the above) \ Medical/surgical/rehabilitation management Prehospital and acute hospital management Medical/surgical management during rehabilitation \ Rehabilitation therapy and management Hospital administrative and personnel issues NOTE. PPS refers to comorbidity adjustment within the prospective payment system. Legend: taken into account, not included, / limited inclusion. Abbreviations: CMG, case-mix group; CSI, Comprehensive Severity Index; Neuro, 5 neurologic parameters (Glasgow Coma Scale score, loss of consciousness, posttraumatic amnesia, pupillary response, computed tomography); SOI, severity of illness subclass of APR-DRGs. diagnosis-related group (DRG) based prospective payment in 1983 energized the search for valid indices of severity. Developers of DRGs chose to rely on computerized hospital discharge abstract data, which became the basis for 1 type of widely used severity indices. The All Patient Refined-Diagnosis Related Groups (APR-DRGs) is a severity index that is based on the coded administrative data in the discharge abstract. The primary data of interest are age, sex, race, and ICD-9-CM codes, which APR-DRGs use in an algorithm to derive DRGs and the severity of illness (SOI) subclass. The APR-DRG SOI is similar in nature to the 4-tiered PPS comorbidity adjustment insofar as it is based on ICD-9-CM diagnostic coding. The SOI uses an ordinal scale from 1 to 4, where 1 is minor severity and 4 is extreme severity. It is inexpensive to use and has performed well in predicting hospital LOS. 4(p17,404-5) A second type of severity index is based on clinical findings documented in the medical record. The Comprehensive Severity Index (CSI) is an index of this variety. It is an extensively validated tool for measuring severity of illness through an algorithm based on clinical examination, radiologic, and laboratory data gathered from the patient s medical record. CSI is a disease-specific index originally developed by a research team at the Johns Hopkins University. Published work by several researchers 4,22,23 has shown CSI severity scores to be among the best predictors of outcomes such as cost, LOS, mortality, resource utilization, and complications. A preliminary report 24 of the correlation between the CSI and costs during the acute hospital stay for a sample of 140 TBI admissions found r equal to.85 (P.001), and for their rehabilitation stay, r equal to.46 (P.001). The high cost of data acquisition currently limits its routine use. Numerous studies of TBI have shown that several neurologic variables have a significant correlation with mortality, morbidity, and long-term outcome. 25 The principal descriptors, which are well-known and readily measured, include initial Glasgow Coma Scale (GCS) score, duration of loss of consciousness (LOC), duration of posttraumatic amnesia (PTA), pupillary response, and initial computed tomography (CT) scan of the brain. Assuming that severity of brain injury is closely associated with a patient s functional limitations at rehabilitation admission, these neurologic descriptors may be considered as part of a criterion standard of TBI outcome predictors against which other potentially useful predictors should be compared. The purpose of this research was to compare the performance of 3 severity of illness indices (1) the CMG from the PPS, (2) the CSI, and (3) the SOI subclass of APR- DRGs and the above 5 neurologic descriptors alone or in combination with the CMG, as predictors of rehabilitation charges and LOS. Charges and LOS serve as proxies for hospital resource use and medical complexity. The CSI and SOI are measures of medical complexity, not just comorbidity. The PPS 4-tier system measures comorbidity only and was not directly compared in this study. Table 1 outlines the many factors that may contribute to hospital resource use, and some of the important differences in the type of data each of the above indices, the PPS comorbidity adjustment, and the 5 neurologic descriptors take into account. METHODS Subject Selection The medical records of patients admitted from 1989 through 1994 to the rehabilitation unit of a level I trauma center with the primary diagnosis of TBI were reviewed by the principal investigator. Subjects with TBI who also had other major acute injuries, such as spinal cord injury or multiple fractures, that were judged to be the primary source of impairment, were not

3 1110 MEASURING MEDICAL COMPLEXITY, Ryser Table 2: Discharge Disposition, Outliers, and Exclusions Discharge Disposition Cohort of 240 Outliers Exclusions Subjects Retained Home * 198 Postacute rehabilitation Other rehabilitation unit SNF Totals Abbreviation: SNF, skilled nursing facility. *Three subjects with LOS 3 days, 3 subjects with interrupted stays of 6 days. included. A total of 240 consecutive TBI admissions were identified. Exclusion criteria were then defined based on factors related to the primary dependent variables, rehabilitation charges, and LOS (table 2). Outliers Subjects were defined as outliers if rehabilitation charges or LOS were at least 2 standard deviations (SDs) greater than the group mean. The decision to use the 2 SD criteria was made to avoid skewing the main trend due to outliers. Ten subjects exceeded the criteria for both charges and LOS, 1 subject exceeded criteria for charges only, and 1 subject exceeded criteria for LOS only. The 12 were excluded from further analysis. Other Exclusions An additional 16 subjects were excluded according to the following criteria: (1) the acute inpatient rehabilitation course was interrupted by an acute illness episode requiring transfer to the intensive care unit (ICU) for 6 days or more (3 subjects) (a long interruption would confound estimation of rehabilitation resource use); (2) the rehabilitation LOS was 3 days or less (3 subjects) (rehabilitation LOS of 3 days or less is generally considered too brief for the interdisciplinary team process to be effective); (3) the patient was transferred to another acute inpatient rehabilitation facility closer to home to complete rehabilitation therapy, having completed less than the mean LOS for the patient s CMG (3 subjects); and (4) the patient was transferred from the inpatient setting to a skilled nursing facility (SNF), having completed less than the mean LOS for the patient s CMG (7 subjects). The exclusion criterion used for transfers to another rehabilitation facility or an SNF is similar to the PPS definition of early transfer published in The intent was to define circumstances in which the patient was unlikely to have received a full course of rehabilitative treatment. For each such early transfer, it can be assumed that rehabilitation goals were not fully accomplished. Subjects transferred after a period greater than the mean LOS were assumed to have completed at least a substantial part of their rehabilitation. As noted in table 2, 8 of the original cohort of 240 subjects were discharged to other rehabilitation units and 11 were discharged to SNFs. Two of these 19 patients were outliers, as defined earlier, and were excluded. Of the 17 remaining transferred patients, 16 were in CMG 5 and had an LOS range of 8 to 57 days. There were 96 subjects in CMG 5, before exclusions, with an average LOS of 37.7 days. Ten of these subjects (3 discharged to rehabilitation units, 7 discharged to SNFs) had LOSs of 8 to 31 days and were excluded based on criteria 3 and 4 above, because LOS was less than the mean LOS for the CMG. Three of those discharged to other rehabilitation units and 4 who went to SNFs had LOSs of 42 to 57 days and were retained in the analysis. The single transferred subject in CMG 3, with an 11-day LOS, was excluded under criteria 3. The mean LOSs for the 212 remaining subjects are listed according to their CMG in table 3. Some analyses were repeated as a check on the robustness of the analyses when possible with outliers included. Descriptive data for the 212 subjects are listed in table 4. Nineteen subjects underwent surgical procedures during their rehabilitation stay and were therefore in APR-DRG 850. The other 193 subjects were defined as belonging in APR-DRG 860. Comparative data for the nonsurgical, surgical, exclusion, and outlier subgroups are listed in table 5. Of the 212 subjects, 198 (93.4%) were discharged directly to home, 7 went to residential postacute rehabilitation facilities or group homes, and 7 were discharged to other acute rehabilitation units closer to home or to SNFs (see table 2). Rehabilitation LOS was carefully reviewed for the 4 subjects who had rehabilitation stays interrupted by transfers to acute care hospital services. Two of the transfers were for elective surgical procedures; their LOSs were 1 and 6 day LOSs. The duration and charges for these interruptions were added to those of the overall rehabilitation stay. The other 2 subjects had severe acute illness during the first 24 hours while on the rehabilitation unit. Data collection for these 2 subjects began when they were readmitted to rehabilitation 3 to 6 days later. Data Gathering Data gathering was preceded by the establishment of objective guidelines and working definitions for key variables by 2 of the principal investigators (DKR, EDB). The records of all 240 original subjects were reviewed completely to confirm eligibility for the TBI criteria and to establish separate and comprehensive records for each subject. These included copies of both the principal acute and rehabilitation hospital records, including history and physicals, discharge summaries, neuroimaging and neuropsychologic reports, and outpatient follow-up records. These subjects and their records have been the basis for multiple published studies involving neuroimaging and neuropsychology outcomes after TBI. 26 Admission FIM scores were rated prospectively for each subject by trained staff members of the rehabilitation treatment team as a part of routine program evaluation. Demographic and neurologic parameter data were gathered retrospectively by Table 3: Rehabilitation LOS by CMG CMG Frequency Mean SD (d) Range (d) Total

4 MEASURING MEDICAL COMPLEXITY, Ryser 1111 Table 4: TBI Subject Descriptors (N 212) Descriptors Mean SD Range Age (y) Initial GCS score LOC (d) PTA (d) Rehabilitation LOS (d) Rehabilitation charges (1994 dollars) 26,599 21, ,772 Education (y) Days postinjury of rehab admission Rehabilitation admission FIM Rehabilitation discharge FIM research assistants who were doctoral candidates in a clinical psychology program who had an interest in TBI and subspecialty competence in neuropsychology. They were trained by the principal investigator to use the prospectively written guidelines. Three of the neurologic parameters (GCS score, LOC, PTA) were recorded by consensus assessment by 3 raters after the raters jointly reviewed the prehospital records, emergency department records, admission records, progress notes from physicians, nurses, and therapists, and, when pertinent, outpatient records. Raters were not blinded to the subjects but used objective criteria and kept records that documented the source of specific data used in scoring the neurologic parameters. Pertinent documentation was found for each of the above variables for every subject. In most instances, multiple sources of information were found for each variable. When 2 or more sources did not agree completely, or a major source was ambiguous, raters used a collaborative approach, in consultation with the principal investigator, to arrive at the best single response. For instance, if a nursing reference to responsiveness to commands contradicted a physician note, a decision was made between one or the other based on the overall clinical context and documentation. To accomplish this optimally, the raters worked simultaneously at the same table, documenting the source data and crosschecking one another s work to arrive at a consensus before final data entry. For the majority of subjects, GCS score, LOC, and PTA were recorded prospectively by the attending physiatrist and documented in the rehabilitation admission history and physical or discharge summary. When clearly stated, these physician records were considered the primary data source. When absent or questionable, documentation by other team members was considered, as listed later. This data-gathering effort was also aided by the fact that many of the physician and nursing records at our study site have been computer-based for several decades. 27 The initial GCS score was defined as the highest score obtained in the first 24 hours postinjury. GCS scoring was complicated by patient intoxication, early sedation, intubation, periorbital trauma, hypoxia, hypotension, and early surgery. Use of the highest score in 24 hours minimized the impact of these common conditions by allowing for adequate stabilization and multiple evaluations over time. In the absence of any nationally accepted guidelines to score the GCS, the suggestions of Marion and Carlier 28 were applied where possible. For the 176 subjects who were admitted to the study facility on the day of injury, data were much more complete. At least 1 initial GCS score was prospectively recorded in more than 95% of prehospital and acute hospital records. More than 50% of subjects had a GCS score recorded hourly by nursing staff in a computer-based record. 27 A reliable highest GCS score in 24 hours was considered difficult to confirm in less than 10% of these records. For the 36 subjects (17% of total) who were initially admitted elsewhere, 10% to 20% of the GCS rating was considered difficult to confirm. In the few instances in which the GCS total was missing, it was calculated from documented observations of the subject s responsiveness. Duration of LOC was defined as the number of days postinjury that the GCS score remained at 8 or less, or until the subject consistently responded to commands. Reliability of the LOC data was similar to that of GCS because it was usually defined by GCS score. Duration of PTA was defined as the number of days postinjury until the patient was consistently fully oriented and able to integrate information from the recent past with the present. PTA duration was determined for more than 80% of subjects while in inpatient rehabilitation, during which multiple team members routinely documented pertinent information. Crosscorrelation of sources was sought specifically in the record. For more than 60% of subjects, PTA duration was documented prospectively by the rehabilitation physician in the discharge summary, based on weekly discussions in the team conference. A hierarchy of information sources was used whether the determination was prospective or retrospective. The team psychologist documented a Galveston Orientation and Amnesia Test (GOAT) score for more than 50% of the subjects. A score of 80 or more was considered the threshold for emerging from PTA. After 1992, the Westmead PTA Scale was administered commonly by speech therapists and used along with other data when the GOAT score was not available. The threshold for emerging from PTA was 3 consecutive Westmead scores of 12 out of 12. When the above formal testing was not available, documented time to recovery of consistent full orientation, and other observations of short-term memory by various team members were used. The above less formal testing was used for most of the 38 subjects for whom PTA did not resolve by the time of discharge, as documented in outpatient medical records or follow-up telephone interviews. Descriptors Table 5: Comparison of Subject Subgroup Descriptors Nonsurgical (n 193) Surgical (n 19) Exclusions (n 16) Outliers (n 12) Age (y) Initial GCS score LOC (d) PTA (d) Rehab LOS (d) Rehab charges (1994 dollars) 22,130 16,647 36,349 22,353 21,843 21, ,798 64,331 NOTE. Values are mean SD.

5 1112 MEASURING MEDICAL COMPLEXITY, Ryser Pupillary response was scored for all subjects by a single rater who reviewed prehospital and ICU records. An abnormal pupillary response was defined by documentation that a pupil (or pupils) was abnormally dilated, constricted, and/or unresponsive to light and that was believed to be a new finding since the TBI. This value was scored as abnormal bilaterally (score, 2), or unilaterally (score, 1), or normal bilaterally (score, 0). Documentation of pupillary response was considered the least reliable of the clinical neurologic parameters because pupillary abnormalities can be subtle and confounded by medications, periorbital trauma, and preexisting conditions. A reference to pupillary response was found for all the 176 subjects admitted directly to our facility but was missing in the acute records for an estimated 5 of the 36 subjects admitted from elsewhere. We relied on the rehabilitation admission physical examination for these subjects. An unknown number of false negatives probably occurred when the initial findings were transient or subtle. Overall, the documentation for pupillary response was considered questionable for less than 10% of the subjects. The initial CT scan score was based on the subject s date of injury CT scan of the brain by using the Traumatic Coma Data Bank 7-level rating scale by Marshall et al. 29 The majority (n 176) of the scans were performed at the study site; the rest were performed at outside referring hospitals where the patients were stabilized before transfer. Although radiologic reports were available on all 212 patients, because of the retrospective investigational design, hard copy or digital data of the CT scans were not available on most patients. For these reasons, we elected to apply the Marshall rating based on the radiologists written reports. To examine the accuracy of this approach, all CT reports were first reviewed and rated according to the Marshall method by 1 graduate student (PG), with spot monitoring by a physician investigator (DKR). A boardcertified radiologist then independently and blindly rated 32 of the available scans. The overall interrater reliability (Spearman rank order) was.88 between these 2 methods of rating CT abnormalities. Subjects in CT classes V and VI were excluded from certain aspects of the analysis, as already explained and noted in table 5. The CSI was scored for all subjects by 2 professional CSI raters who train other CSI raters. To meet the standard required for accurate CSI data collection, a random sample of 20 cases from both trainers was rescored blindly by a different trainer for reliability testing. Greater than 95% agreement at the criterion level with the second trainer was found for all reliability cases. The CSI classifies patients according to the degree of abnormality of individual signs and symptoms of a patient s disease or diseases as observed by medical personnel through physical examination, laboratory data, and radiographic information. Thus, the CSI is based on objective clinical measures of a patient s condition, not just on coded diagnostic information (ICD-9-CM coding). The more abnormal the signs and symptoms, the higher the score. For the discrete CSI score, severity ranges from normal to mild findings (level 1), to moderate symptoms (level 2), severe symptoms (level 3), and to signs and symptoms being catastrophic, life-threatening, or likely to result in organ failure (level 4). CSI severity is also represented as a continuous scale without a preset maximum limit. The maximum CSI is a composite of the most aberrant findings observed throughout the rehabilitation admission regardless of when they occur. We also obtained admission and discharge CSI scores based on findings during the first 24 hours after admission and before discharge. Only maximum CSI, both continuous and discrete, are reported here because it measures medical complexity throughout the admission, as did the other severity indices. A trained rater spent about 15 minutes per chart to collect all the data required for admission, maximum, and discharge CSI scores for each subject. A variable called the health history code was defined and scored by a single rater through review of the acute hospital and rehabilitation histories and discharge summaries; it represents the subject s premorbid health and functional history. The scale was graded as follows: A, good premorbid health with no functional limitations; B, mild to moderate functional limitations because of chronic health problems; C, serious but not incapacitating chronic disease with restricted activity; and D, severe restriction of activity because of chronic disease, including people who are institutionalized and dependent. This variable was dichotomized as healthy (A) versus impaired (B, C, D) for the purposes of data analysis because of the infrequent incidence of the higher levels of premorbid activity restrictions. Only 6 subjects were in categories C and D. Forty-four were in category B. APR-DRG SOI subclass levels 1 to 4 were determined by a software program developed by 3M known as a Grouper, version 15. a Grouper uses administrative data routinely extracted by professional coders in the hospital medical records department. These data include ICD-9-CM codes for the patient s principal and secondary diagnoses and procedures and patient age. Cross-tabulation of SOI and discrete CSI levels by CMG is reported in table 6. It is evident that most SOI scores are concentrated in levels 2 and 3, whereas the CSI shows a much wider spread. SOI was available for all subjects. However, the subject number was reduced for some regressions involving the APR-DRGs because subjects in the surgical APR-DRG were too few to allow for separate analysis, as noted in table 5. Both rehabilitation LOS and charges were used as dependent variables because both were available. Charges would be expected to be a better proxy for use of hospital resources than LOS, although they were highly correlated (r.963, N 212). Many large multicenter studies use LOS only because of the numerous uncontrolled factors that affect charges among institutions and across regions of the United States. 19 Some of these concerns were eliminated in this study because all subjects were treated in a single facility. Hospital charges were used in preference to cost because cost accounting is considered more subject to internal accounting methods than charges at our institution. Charges did not include physician charges because these data were not readily available. Variation in management practices of the treatment team during this time period was believed to be small because of relatively low turnover and consistency in management practices. There was no turnover in medical, nursing, or administrative directors during this time period. There was no dedicated TBI treatment team and it was not feasible to adjust for possible variation among clinicians because of the large number of team members involved. Figures 1, 2, and 3 show the mean LOS, continuous CSI, and FIM total at discharge, respectively, by discharge year. The figures show marked changes in LOS and charges in the first 3 years, followed by a steady decline in LOS but a minimal change in CSI levels and improved FIM scores. This decrease in LOS is a potential confounding factor that is addressed in the discussion section. The sample sizes were too small to adjust for LOS changes by year in the analysis. Statistical Methods Statistical analyses were performed with SPSS, version b Charges were inflation adjusted to 1994 dollars ac-

6 MEASURING MEDICAL COMPLEXITY, Ryser 1113 Table 6: Cross-Tabulation of CMG Versus SOI or CSI SOI CSI CMG Total Total Total, % 5, , , , , , , , , , 100 cording to data from the US Bureau of Labor Statistics, Consumer Price Index for Medical Care in the Urban West Area. We used ordinary multiple regression techniques on the 212 subjects (after exclusions and outliers were removed) to evaluate the contribution of measures of severity for predicting resource use during the rehabilitation stay. Normality was assessed by P-P plots. Heteroscedasticity was evaluated by plots of residuals versus predicted values. Multicolinearity was checked by use of tolerances and variance inflation factors. Linearity was assessed by plotting the data and residuals. Certain regressions were repeated, including outliers with and without logarithmic transformations, and results were similar to those shown here. The variables indicated in each regression in the tables were entered as a block to address specific a priori hypotheses. No regressions were stepwise. We report adjusted R 2 to reduce bias in R 2 due merely to adding an additional variable. 30 Use of a cross-validation R 2 as computed by Stineman et al 19 was considered, but the sample size made this approach infeasible. The neurologic parameters were entered as a block into the regressions to contrast the R 2 values of the formally validated indices against the R 2 from a straightforward and available set of neurologic severity indicators. Sparseness of cells produced instabilities in some of the initial regression models. This was addressed for CMG and APR- DRG SOI by collapsing levels 1 and 2 for each of these 2 indices. The final regression models passed the standard checks for multicolinearity and other numeric instabilities. We used the change in adjusted R 2 to determine (1) the ability to predict charges in nonsurgical cases because of each severity measure while adjusting for age and health history and (2) the explanatory ability of a severity measure in combination with the CMG to predict charges or LOS in surgical and nonsurgical cases. In each model, adjusted R 2 could be considered the proportion of variation in charges or LOS that was explained by the regression model. RESULTS Figure 4 is a scatterplot of rehabilitation CSI versus rehabilitation charges (adjusted R for the model with only maximum CSI as a predictor). Using the base model that included patient age and dichotomized health history, the percentage of variation in rehabilitation charges explained by the variables, singly and in combination is shown in table 7. Only subjects in the nonsurgical APR-DRG were used for these regressions to make possible direct comparison with APR- DRG SOI, which separates rehabilitation patients into surgical and nonsurgical groups. The sample size was further reduced for regressions involving CT classification because subjects with hematomas greater than 25mL in volume, but not evacuated (level V), or who underwent craniotomy (level VI), logically could not be considered part of the ordinal scale. CT levels I to IV is an ordinal scale that measures increasing levels of intraparenchymal lesions and cerebral edema. CT level VII is associated with a more severe outcome than level IV (Bigler ED, unpublished data), allowing this CT level to be included in the ordinal scale for analysis. The 31 subjects with a CT level V or VI were therefore excluded from calculations involving CT class. Auxiliary analyses showed that CT levels V and VI were not significantly predictive of charges when health history and age were adjusted. Table 7 shows that the CMG, continuous CSI, and PTA had the greatest individual ability to explain charges (.349,.293,.260, respectively). LOC and GCS score had intermediate (.108,.107, respectively) and SOI, CT, and pupillary response had low (.062,.046,.018, respectively) ability to explain charges. Combining all 5 of the neurologic parameters yielded Fig 1. Means of rehabilitation LOS (in days) by rehabilitation discharge year (N 212). Fig 2. Means of continuous CSI by rehabilitation discharge year (N 212).

7 1114 MEASURING MEDICAL COMPLEXITY, Ryser Table 7: Predictive Abilities of Combined or Single Variables for Charges* Variables Adjusted R 2 N Fig 3. Means of discharge FIM total by rehabilitation discharge year (N 212). an adjusted R 2 only slightly better than PTA alone (.275 vs.260). The best explanation of charges was achieved by the CMG adjusted by a severity index. The best severity adjusted model included the CMG and continuous CSI, which fully explained.446 of the variance in charges when adjusted for age and health history. The combined CMG with the neurologic parameters explained.431 of the variation. Table 8 shows the effect of adding a severity adjustment to the CMG for the full cohort of 212 patients unadjusted for age or health history, including those who had surgical procedures during their rehabilitation stay. The variation in charges was consistently somewhat better explained by these models than the variation in LOS. Continuous CSI combined with the CMG yielded the highest explanation of the variance of charges (.466), that is, 25.6% higher than the CMG alone (.371). The variance with discrete CSI plus the CMG was modestly higher than SOI combined with the CMG or the CMG alone (.402,.378,.371, respectively). DISCUSSION The best single variable for predicting rehabilitation charges or LOS for nonsurgical patients in this cohort was the CMG. This should not be surprising because this is exactly what CMGs were created to do. The duration of the rehabilitation stay is determined by a complex interplay of factors related to the patient before and after the injury and during the course of CMG continuous CSI CMG, PTA, LOC, GCS, CT, pupillary response CMG discrete CSI CMG SOI PTA, LOC, GCS, CT, pupillary response CMG Continuous CSI PTA LOC GCS SOI CT Pupillary response *Regression base model included age and health history. Proportion of variation explained for charges. Nineteen subjects in the surgical APR-DRG were excluded for this analysis. CT classes V and VI were excluded, reducing N by 31 subjects. hospital management, as described in table 1. As expected, by the time a patient is admitted to rehabilitation, his/her functional status (eg, ability to move, use hands, think, communicate, control bodily functions) is a more important determinant of rehabilitation LOS, for most patients, than any other easily quantified factors at that stage. Medical severity would be expected to play an important secondary role to functional status in determining rehabilitation resource use and outcome for most patients. The situation is different during the acute hospitalization, when medical severity is the primary determinant of cost and LOS. 24 During the rehabilitation stay, the continuous CSI score appears to account for the most important clinical findings in a single score. PTA is a close second according to these data, but PTA cannot be expected to account for factors unrelated to the brain injury and thus would make a poor index of medical complexity. The predictive power of the 5 combined neurologic parameters was close to that of CSI, but they also do not describe nonneurologic medical complexity. SOI alone had a much lower R 2 with rehabilitation charges than did the CSI alone. This was expected because SOI represents only coded diagnostic data and not the variety of other clinical information that may differentiate the severity in patients with the same diagnosis. By its nature, SOI can describe these factors (eg, presence or absence of pneumonia) only qualitatively with no measure of the quantity of the clinical entity (eg, severity of pneumonia as described by white blood cell count, organism, fever, oxygen saturation, chest radiograph). The CSI incorporates many neurologic parameters as a measure of the severity of brain injury as well as nonneurologic conditions. Table 8: Predictive Ability of CMG Alone or Combined With SOI Indices (N 212) Fig 4. Relationship of rehabilitation charges in 1994 dollars to the continuous CSI (N 212, r ). Indices Adjusted R 2 for Charges* Adjusted R 2 for LOS CMG continuous CSI CMG discrete CSI CMG SOI CMG *Proportion of variation explained for charges or LOS.

8 MEASURING MEDICAL COMPLEXITY, Ryser 1115 Inclusion of medical and surgical management information in SOI, by separating patients according to who had surgical procedures and who did not, improves correlation with resource use but this may not be desirable for outcome studies. The data cannot address whether the surgery was appropriate or improved the outcome. From a clinical practice improvement perspective, a severity index that does not include a process variable such as surgery in the severity measurement would be desirable because the process itself may affect severity in unpredictable ways. Limitations of our study include its retrospective design, relatively low number of subjects, and the large number of uncontrolled variables. The assumption that medical complexity is proportional to hospital resource use potentially could be invalidated by large variations in subjects premorbid characteristics and in variations in medical, surgical, and rehabilitation management, which none of the 3 indices studied here address (see table 1). The assumption that rehabilitation hospital charges and LOS are proportional to hospital resource use is also limited by hospital administrative and personnel issues that affect LOS and charges, as noted in table 1. The documented significant variation in LOS and severity over the time of data collection is also a potential confounding factor. Low mean LOS and mean CSI during 1989 is readily explained by the fact that it was the first year of operation for the rehabilitation unit and patients with the most severe TBIs still tended to go to other well-established community facilities. This was not the case in subsequent years. The year 1990 was the year during which the cost basis for all future Medicare reimbursements was established under the Tax Equity and Fiscal Responsibility Act. Admission of patients with the most severe TBIs with high medical complexity and high-expected resource use was high that year, which is reflected by the data for charges and the CSI. From 1991 to 1994, there was a steady decline in LOS whereas the CSI changed very little. At the same time, the level of functional outcome on discharge showed some improvement, as measured by the discharge FIM total. The cause of the previously described combination of LOS findings is unclear. CSI data provide evidence that declining medical complexity was not the explanation for the shorter LOS. Discharge FIM data suggest that functional outcome was not adversely affected by premature discharge. Other aspects of outcome quality were not measured, and no information about the acute and rehabilitation clinical management was collected. No systematic program or overt pressure was applied at our not-for-profit facility to reduce LOS or to increase profits, other than a desire of both clinicians and administrators to be more efficient and effective. The decrease in LOS followed a welldocumented nationwide trend in both Traumatic Brain Injury Model Systems data 3 and UDS data during that period. Mean LOS for TBI reported by UDS in 1990, which does not exclude outliers, was 46 days. 31 It steadily declined to 34 in The average LOS for TBI at the study facility, with outliers excluded, stabilized at approximately 19.6 days between 1996 and 2002, which is approximately 20% below the average for 1989 to These data suggest that changes in clinical management during acute and/or rehabilitation hospitalizations are the most likely explanation for the dramatic improvement in rehabilitation efficiency in this time period. Rehabilitation factors might include improved inpatient rehabilitation team confidence in planning earlier discharges due to increased availability and quality of outpatient services, greater emphasis on hospital efficiency and cost savings, and changes in rehabilitation team decision processes and expectations that have been recalibrated to shorter LOSs. The answer is complex and multifactorial, involving the business, the science, and the art of neurorehabilitation. The 20% decrease in LOS over the last decade may raise questions about the validity of the results with respect to current rehabilitation practice with shorter LOSs and steadily increasing charges. From a statistical viewpoint, this does not affect the study results. The relative R 2 value of dependent variables in multiple regressions does not change based on an incremental change in the dependent variable (charges have increased and LOS decreased). The absolute value of the R 2 values may change, however. The fact that rehabilitation practice may have changed significantly since the data were gathered a decade ago does not compromise the analysis or the results of this comparison of 3 indices of medical severity. The way in which services are reimbursed can markedly shape the way they are delivered. PPS reimbursement methodology clearly will influence admission and transfer policies of rehabilitation units. The ideal reimbursement method offers incentives to provide the best medical rehabilitation practices at the lowest cost. PPS currently treats all facilities the same with respect to reimbursement for complications and comorbidities. It offers compensation for only the single most severe comorbidity. It is based on empiric sampling of data from thousands of patients, but the types and levels of conditions it adjusts for appear counterintuitive in some instances. Its overall effect may be to reward all rehabilitation units that behave similarly with respect to admissions of medically complex patients. This may not be desirable. Major differences exist between the manner in which they handle medical complexity and emergencies. A freestanding rehabilitation facility located miles from the nearest acute care hospital or a distinct part-unit in a community hospital cannot be expected to provide the same level of medical services that is provided in a rehabilitation unit integrated into a tertiary care hospital. Rehabilitation referrals within a tertiary care hospital or level I trauma facility would be expected to differ with respect to medical complexity compared with referrals in other rehabilitation settings. We did not compare the 4-tier PPS comorbidity index with the CSI and SOI in this study. It is likely, however, that it would not fare much better than the 1.9% of additional variance explained by the comorbidity diagnostic information tested by Stineman et al. 19 Data from a multisite stroke rehabilitation study, 32 which included our facility, compare the CSI with the 4-tier PPS comorbidity index. The difference in the explanation of variation in rehabilitation LOS in the stroke data between the CSI and the 4-tier index is in the same direction and of an even larger magnitude than we found between the CSI and SOI in this study. These data provide additional external support of our results but in a different rehabilitation impairment category. Administrative data alone have significant limitations in assessing health care quality and complexity but they may be suitable for tracking other aspects of health care needs of people with disabilities. 33,34 An index of medical complexity based on objective clinical data avoids a variety of assumptions, historical misconceptions, coding errors, and other problems inherent with classification systems based on diagnostic codes. Studies that have compared the performance of clinical versus administrative data for measuring health care outcomes support the relative advantages of clinical information. 4(p261) A few key clinical variables, however, may provide much of the explanatory power of a comprehensive severity index such as the CSI at a fraction of the $6.25 per case it cost to score the complete CSI (based on estimated labor costs in 1995). In our study, PTA alone conveyed most of the explanatory power of all 5 neurologic variables. The explanation of variance for all 5

9 1116 MEASURING MEDICAL COMPLEXITY, Ryser neurologic variables combined with the CMG was comparable to the explanation of variance of the CSI and CMG for this cohort. This result in a TBI population indicates how predominant the brain injury is as a predictor of resource use. An advantage of a comprehensive index such as the CSI is that it is comparable across rehabilitation impairment categories and other medical populations. A comprehensive severity index based on clinical data may serve as a criterion standard against which other less expensive methodologies for risk adjustment can be compared as they evolve. For the time being, administrative data are the practical solution to the complex problem of describing medical complexity in rehabilitation, just as in other settings. Cost and accuracy of obtaining extensive clinical information remain formidable challenges. The cost of collecting clinical data for a severity index such as the CSI must be weighed against its benefits. The additional cost of scoring the CSI for this study, as compared with the APR-DRG, accounted for less than 2% of the total cost of the study. The additional predictive power of the CSI, as shown in this study, may outweigh its added cost in appropriate applications. As hospital information technology improves, the potential to use objective clinical data to derive an index of medical complexity for rehabilitation populations should improve also. It is likely that clinicians will prefer clinical data as the foundation for administering and improving complex clinical processes such as rehabilitation. Practical limits to hospital resources, organization, and technology will slow the introduction of clinical data gathering for severity measurement, but the advantage of basing medical rehabilitation practice improvement on clinical data (pathophysiology) rather than administrative data (historical and expert opinion based) is strong and forward looking. Use of severity of illness indexes in a rehabilitation setting warrants further investigation. The data we used in this study are now a decade old and rehabilitation practices have already changed significantly, as evidenced by the change in average LOS. Similar research with more recent data, larger populations, other rehabilitation impairment categories, and a variety of practice settings is needed. Direct comparison of the PPS 4-tier system should be made with other severity indices. The PPS was designed to evolve as it creates its own database on which to build a more refined comorbidity adjustment methodology. Performance of future versions of comorbidity adjustments within the PPS should also be compared with a comprehensive clinically based severity index. CONCLUSIONS Medical complexity is an important determinant of rehabilitation hospital resource use after TBI. The ability to accurately describe medical complexity is important for clinical research and as a component of the newly introduced government methodology for reimbursing hospitals for Medicare patients during inpatient rehabilitation. This is the first reported study comparing the performance of 3 medical severity indices for measuring medical complexity in a rehabilitation population. The CSI, based on clinical information in the medical record, was the best modifier of the CMG, among the factors we considered, to predict rehabilitation resource use, represented by rehabilitation charges and LOS. It performed considerably better for this purpose than did a severity index (APR-DRG SOI) based solely on coded diagnostic data. An index of medical complexity based on objective clinical information may have distinct advantages over indices based on coded diagnostic data for interpreting outcome studies, improving clinical processes, and devising an equitable reimbursement methodology for medical rehabilitation. Acknowledgments: Thanks to Lisa Barker, PhD, Jenny Bartholomew, PhD, Michael Brooks, PhD, Antonietta Russo, PhD, Elona Suli, MD, and Elisabeth Wilde, PhD, for data collection and T. Andrew Dodds, MD, for manuscript review. References 1. Novack TA, Bush BA, Meythaler JM, Canupp K. Outcome after traumatic brain injury: pathway analysis of contributions from premorbid, injury severity, and recovery variables. Arch Phys Med Rehabil 2001;82: Kreutzer JS, Kolakowsky-Hayner SA, Ripley D, et al. Charges and lengths of stay for acute and inpatient rehabilitation treatment of traumatic brain injury Brain Inj 2001;15: High WM. Factors affecting hospital length of stay and charges following traumatic brain injury. 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