Clinical Classifications Software Categories Research Articles

Use of clinical classifications software to address ICD coding transition in large healthcare databases analyzed via high-dimensional propensity scores.

The EUPAS26595 study characterized the rate of acute renal failure (ARF) in patients exposed to levetiracetam versus other antiepileptic drugs using healthcare claims data and a high-dimensional propensity score (hd-PS) for confounding adjustment. The data contained several coding systems by design and an update in International Classification of Diseases (ICD) coding dictionary. Such coding heterogeneity can affect the performance of hd-PS, and manually coding harmonization is not feasible. Our objective was to explore the impact of code aggregation via Clinical Classifications Software (CCS) on the analysis of a large claims-based database using hd-PS. Patients with epilepsy, who were new-users of an antiepileptic drug, were identified from the IBM® MarketScan® Research Databases. We used CCS categories to harmonize coding and compared the results with other alternatives. Incidence rate ratios (IRRs) were computed using modified Poisson regression model with a robust variance estimator. For January 2008-October 2015 (before ICD update), 34 833 eligible patients initiated levetiracetam and 52 649 initiated a comparator drug; IRR (95% CI) for ARF for the hd-PS analysis was 1.34 (0.72-2.50) without CCS categories and 1.30 (0.71-2.39) with CCS categories. For January 2008-December 2017 (including ICD coding change), 45 672 eligible patients initiated levetiracetam and 64 664 initiated a comparator drug; IRR (95% CI) for the hd-PS analysis was 1.34 (0.78-2.29) without CCS categories and 1.37 (0.80-2.34) with CCS categories. Using single-level CCS categories to overcome differences in coding provides consistent results and can be used in studies that use large claims data and hd-PS for adjustment.

Medical Costs of Substance Use Disorders in the US Employer-Sponsored Insurance Population

Direct costs of substance use disorders (SUDs) in the United States are incurred primarily among the working-age population. Quantifying the medical cost of SUDs in the employer-sponsored insurance (ESI) population can improve understanding of how SUDs are affecting workplaces and inform decision-making on the value of prevention strategies. To estimate the annual attributable medical cost of SUDs in the ESI population from the health care payer perspective. In this economic evaluation, Merative MarketScan 2018 databases were weighted to represent the non-Medicare eligible ESI population. Regression and mathematical modeling of medical expenditures controlled for insurance enrollee demographic, clinical, and insurance factors to compare enrollees with and without an SUD diagnosis to identify the annual attributable medical cost of SUDs. Data analysis was conducted from January to March 2022. International Statistical Classification of Diseases, Tenth Revision, Clinical Modification SUD diagnoses on inpatient or outpatient medical records according to Clinical Classifications Software categories (alcohol-, cannabis-, hallucinogen-, inhalant-, opioid-, sedative-, stimulant-, and other substance-related disorders). Annual SUD medical cost in the ESI population overall and by substance type (eg, alcohol). Number of enrollees with an SUD diagnosis and the annual mean cost per affected enrollee of SUD diagnosis (any and by substance type) are also reported. Among 162 million ESI enrollees, 2.3 million (1.4%) had an SUD diagnosis in 2018. The regression analysis sample included 210 225 individuals with an SUD diagnosis (121 357 [57.7%] male individuals; 68 325 [32.5%] aged 25-44 years) and 1 049 539 individuals with no SUD diagnosis. The mean annual medical cost attributable to SUD diagnosis per affected enrollee was $15 640 (95% CI, $15 340-$15 940), and the total annual medical cost in the ESI population was $35.3 billion (2018 USD). Alcohol use disorder ($10.2 billion) and opioid use disorder ($7.3 billion) were the most costly. In this economic evaluation of medical expenditures in the ESI population, the per-person and total medical costs of SUDs were substantial. Strategies to support employees and their health insurance dependents to prevent and treat SUDs can be considered in terms of potentially offsetting the existing high medical cost of SUDs. Medical expenditures for SUDs represent the minimum direct cost that employers and health insurers face because not all people with SUDs have a diagnosis, and costs related to absenteeism, presenteeism, job retention, and mortality are not addressed.

Mapping three versions of the international classification of diseases to categories of chronic conditions.

IntroductionAdministrative health data capture diagnoses using the International Classification of Diseases (ICD), which has multiple versions over time. To facilitate longitudinal investigations using these data, we aimed to map diagnoses identified in three ICD versions – ICD-8 with adaptations (ICDA-8), ICD-9 with clinical modifications (ICD-9-CM), and ICD-10 with Canadian adaptations (ICD-10-CA) – to mutually exclusive chronic health condition categories adapted from the open source Clinical Classifications Software (CCS).MethodsWe adapted the CCS crosswalk to 3-digit ICD-9-CM codes for chronic conditions and resolved the one-to-many mappings in ICD-9-CM codes. Using this adapted CCS crosswalk as the reference and referring to existing crosswalks between ICD versions, we extended the mapping to ICDA-8 and ICD-10-CA. Each mapping step was conducted independently by two reviewers and discrepancies were resolved by consensus through deliberation and reference to prior research. We report the frequencies, agreement percentages and 95% confidence intervals (CI) from each step.ResultsWe identified 354 3-digit ICD-9-CM codes for chronic conditions. Of those, 77 (22%) codes had one-to-many mappings; 36 (10%) codes were mapped to a single CCS category and 41 (12%) codes were mapped to combined CCS categories. In total, the codes were mapped to 130 adapted CCS categories with an agreement percentage of 92% (95% CI: 86%–98%). Then, 321 3-digit ICDA-8 codes were mapped to CCS categories with an agreement percentage of 92% (95% CI: 89%–95%). Finally, 3583 ICD-10-CA codes were mapped to CCS categories; 111 (3%) had a fair or poor mapping quality; these were reviewed to keep or move to another category (agreement percentage = 77% [95% CI: 69%–85%]).ConclusionsWe developed crosswalks for three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) to 130 clinically meaningful categories of chronic health conditions by adapting the CCS classification. These crosswalks will benefit chronic disease studies spanning multiple decades of administrative health data.

Assessment of Annual Cost of Substance Use Disorder in US Hospitals

A persistently high US drug overdose death toll and increasing health care use associated with substance use disorder (SUD) create urgency for comprehensive estimates of attributable direct costs, which can assist in identifying cost-effective ways to prevent SUD and help people to receive effective treatment. To estimate the annual attributable medical cost of SUD in US hospitals from the health care payer perspective. This economic evaluation of observational data used multivariable regression analysis and mathematical modeling of hospital encounter costs, controlling for patient demographic, clinical, and insurance characteristics, and compared encounters with and without secondary SUD diagnosis to statistically identify the total attributable cost of SUD. Nationally representative hospital emergency department (ED) and inpatient encounters from the 2017 Healthcare Cost and Utilization Project Nationwide Emergency Department Sample and National Inpatient Sample were studied. Statistical analysis was performed from March to June 2020. International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) principal or secondary SUD diagnosis on the hospital discharge record according to the Clinical Classifications Software categories (disorders related to alcohol, cannabis, hallucinogens, inhalants, opioids, sedatives, stimulants, and other substances). Annual attributable SUD medical cost in hospitals overall and by substance type (eg, alcohol). The number of encounters (ED and inpatient) with SUD diagnosis (principal or secondary) and the mean cost attributable to SUD per encounter by substance type are also reported. This study examined a total of 124 573 175 hospital ED encounters and 33 648 910 hospital inpatient encounters from the 2017 Healthcare Cost and Utilization Project Nationwide Emergency Department Sample and National Inpatient Sample. Total annual estimated attributable SUD medical cost in hospitals was $13.2 billion. By substance type, the cost ranged from $4 million for inhalant-related disorders to $7.6 billion for alcohol-related disorders. This study's results suggest that the cost of effective prevention and treatment may be substantially offset by a reduction in the high direct medical cost of SUD hospital care. The findings of this study may inform the treatment of patients with SUD during hospitalization, which presents a critical opportunity to engage patients who are at high risk for overdose. Aligning incentives such that prevention cost savings accrue to payers and practitioners that are otherwise responsible for SUD-related medical costs in hospitals and other health care settings may encourage prevention investment.

Comparison of Telemedicine Versus In-Person Visits on Impact of Downstream Utilization of Care.

Background: Telemedicine use has expanded substantially in recent years. Studies evaluating the impact of telemedicine modalities on downstream office visits have demonstrated mixed results. Introduction: We evaluated insurance claims of a large commercial payer, Blue Cross Blue Shield of Michigan (BCBSM), to assess the frequency of follow-up visits following encounters initiated via telemedicine versus in-person. Materials and Methods: We used the BCBSM claim-level data set (2011-2017) to assess encounters in the following places of service: hospital outpatient, doctor's office, patient's home, or psychiatric daycare facility. We identified the primary diagnostic category for 30-day episodes of care using clinical classifications software (CCS) and multilevel clinical classifications software (ML-CCS). Our intervention group consisted of episodes initiated via telemedicine; our control group consisted of episodes initiated in-person. Our primary outcome was the percentage of 30-day episodes with a related visit (encounters occurring within the same period and CCS categories) across CCS categories. Our secondary outcome was the mean related visit rate. Results: The final data set included 4,982,456 patients and 68,148,070 claims, of which 53,853 were telemedicine related. Many episodes did not have related visits (the mean related visit rate was 16%). Telemedicine visits had a higher frequency of related visits across all CCS categories. Discussion: Episodes of care initiated via telemedicine more frequently generate related visits within a 30-day period. This increased health care utilization could represent excessive care or could reflect expanded access to care. Conclusion: Further research should explore the cause of this increased utilization and potential unintended consequences.

Policy Implications for the COVID-19 Pandemic in Light of Most Patients (≥72%) Spending at Most One Night at the Hospital After Elective, Major Therapeutic Procedures.

A large number of inpatients with Coronavirus disease 2019 (COVID-19) in some regions of the United States may interfere with the ability of hospitals to take care of patients requiring treatment for other conditions. Nonetheless, many patients need surgery to improve their quality of life and to prevent deterioration in health. Curtailment of services also negatively affects the financial health of hospitals and health systems. Broad policies to prohibit all “elective” surgical procedures to ensure that there is sufficient hospital capacity for pandemic patients may be unnecessarily restrictive because, for many such procedures, patients are rarely admitted following surgery or only stay overnight. We studied all elective inpatient and ambulatory cases involving major therapeutic procedures performed in the state of Florida in 2018. We mapped the primary procedure to the corresponding Clinical Classification Software (CCS) category. We determined the distributions of lengths of stay overall and as stratified by CCS category, then calculated the percentage of cases that had a hospital length of stay of ≤1 night (i.e., 0 or 1 day). A threshold of one night was selected because patients discharged home on the day of surgery have no effect on the inpatient census, and those staying overnight would either have a transient effect or no effect if observed overnight in the postoperative care unit. Among the 1,852,391 elective cases with one or more major therapeutic procedures, 65.2% (95% lower confidence limit [LCL] = 65.1%) of cases had a length of stay of 0 days and 72.9% (95% LCL = 72.8%) had stay ≤1 day. There were 38 different CCS categories for which at least 95% of patients had a length of stay of ≤1 day. There were 28 CCS codes that identified 80% of the patients who were discharged with a length of stay ≤1 day, showing representation of multiple surgical specialties. Our results show that even in the face of constraints imposed by a high hospital census, many categories of major therapeutic elective procedures could be performed without necessarily compromising hospital capacity. Most patients will be discharged on the day of surgery. If overnight admission is required, there would be an option to care for them in the postanesthesia care unit, thus not affecting the census. Thus, policies can reasonably be based on allowing cases with a substantial probability of at most an overnight stay rather than a blanket ban on “elective” surgery or creating a carve-out for specified surgical subspecialties. Such policies would apply to at least 72% of elective, major therapeutic surgical procedures.

Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization.

ObjectiveTo compare different definitions of multimorbidity to identify patients with higher health care resource utilization.Patients and MethodsWe used a multinational retrospective cohort including 147,806 medical inpatients discharged from 11 hospitals in 3 countries (United States, Switzerland, and Israel) between January 1, 2010, and December 31, 2011. We compared the area under the receiver operating characteristic curve (AUC) of 8 definitions of multimorbidity, based on International Classification of Diseases codes defining health conditions, the Deyo-Charlson Comorbidity Index, the Elixhauser-van Walraven Comorbidity Index, body systems, or Clinical Classification Software categories to predict 30-day hospital readmission and/or prolonged length of stay (longer than or equal to the country-specific upper quartile). We used a lower (yielding sensitivity ≥90%) and an upper (yielding specificity ≥60%) cutoff to create risk categories.ResultsDefinitions had poor to fair discriminatory power in the derivation (AUC, 0.61-0.65) and validation cohorts (AUC, 0.64-0.71). The definitions with the highest AUC were number of (1) health conditions with involvement of 2 or more body systems, (2) body systems, (3) Clinical Classification Software categories, and (4) health conditions. At the upper cutoff, sensitivity and specificity were 65% to 79% and 50% to 53%, respectively, in the validation cohort; of the 147,806 patients, 5% to 12% (7474 to 18,008) were classified at low risk, 38% to 55% (54,484 to 81,540) at intermediate risk, and 32% to 50% (47,331 to 72,435) at high risk.ConclusionOf the 8 definitions of multimorbidity, 4 had comparable discriminatory power to identify patients with higher health care resource utilization. Of these 4, the number of health conditions may represent the easiest definition to apply in clinical routine. The cutoff chosen, favoring sensitivity or specificity, should be determined depending on the aim of the definition.

Finding Chances to Intervene Before the Fatal Overdose: Linking ED and Mortality Data

ObjectiveLink emergency department (ED) with death certificate mortality data in order to examine the prior medical history of opioid overdose victims leading up to their death.IntroductionIn 2017, 951 Missouri residents died from an opioid overdose—a record number for the state.1 This continues the trend from 2016, which saw an increase of over 30% in opioid overdose deaths compared to 2015. The Missouri Department of Health and Senior Services (MDHSS) manages several public health surveillance data sources that can be used to inform about the opioid epidemic. Opioid overdose deaths are identified through death certificates which are collected through the vital records system. MDHSS also manages the Patient Abstract System (PAS), which contains ED and inpatient hospitalization data from approximately 132 non-federal Missouri hospitals. PAS contains about 130 variables, which include demographic data, diagnoses codes, procedures codes, and other visit information. Records can have up to 23 diagnosis fields, which are coded using ICD-10-CM (International Classification of Diseases, Clinically Modified). The first diagnosis field is the primary reason for a visit.MethodsLinkage and analysis of the data was performed using SAS Enterprise Guide 6.1. Opioid overdose deaths were identified through ICD-10 analysis looking for drug poisoning underlying cause of death codes and opioid-specific codes found in the multiple cause (contributing cause) of death fields. Table 1, below, summarizes the ICD-10 codes used. Mortality data from the 951 decedents were linked to ED data from 2016 and 2017. Records were linked using multiple passes over the ED records. Records were first linked on social security number. Following this linkage, ED records with no initial match went through a second pass and linked on name and date of birth. Finally, a third pass for records still without a match was conducted using date of birth, census tract, and sex. After these passes, the linkages were reviewed to identify any false positives. The 23 diagnosis fields contained in PAS were analyzed to look for patterns in diagnosis coding. ICD-10-CM codes were too broad so CCS (Clinical Classifications Software) categories were utilized.ResultsIn total, 3,500 ED records were linked to the 951 decedents. After removing false positives, the total number of ED records was 3,357. Approximately 70% (687) of decedents were linked to at least one ED record. One hundred and eighty-eight visits were due to drug overdose (153 opioid overdoses). The most common primary diagnosis CCS categories (category numbers in parentheses) were: substance-related disorders (661), Spondylosis; intervertebral disc disorders; other back problems (205), abdominal pain (251), and other nervous system disorders (95). Collectively, these four categories represented over 20% of all primary diagnoses. Across all 23 diagnosis fields there were similar results. The most common CCS categories were as follows: substance-related disorders (661), other aftercare (257), essential hypertension (98), and mood disorders (657). Pie charts (Fig. 1 and 2) below show proportions of CCS categories across all diagnoses fields and primary diagnosis broken into three major categories: pain/injury, substance abuse/mental health, and other. In order to reduce the impact of CCS categories with small numbers, these graphics represent only CCS categories that made up 1% or more of the total collection of diagnoses codes. Of the 687 decedents that were matched successfully to ED records, 96% had at least one pain/injury or one substance abuse/mental health ICD-CM code in at least one record, and 68% had both.ConclusionsThese findings suggest that many overdose decedents visited the ED in the years prior to death. Many of these visits were not due to an overdose; however, they could be indicative of a problem with opioids (i.e. pain, drug-seeking, substance use-related). ED staff and public health professionals could utilize these opportunities to refer patients to recovery services and recommend they heed caution when using opioids.

Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

Accurate risk adjustment is the key to a reliable comparison of cost and quality performance among providers and hospitals. However, the existing case-mix algorithms based on age, sex, and diagnoses can only explain up to 50% of the cost variation. More accurate risk adjustment is desired for provider performance assessment and improvement. To develop a case-mix algorithm that hospitals and payers can use to measure and compare cost and quality performance of their providers. All 6,048,895 patients with valid diagnoses and cost recorded in the US Veterans health care system in fiscal year 2016 were included in this study. The dependent variable was total cost at the patient level, and the explanatory variables were age, sex, and comorbidities represented by 762 clinically homogeneous groups, which were created by expanding the 283 categories from Clinical Classifications Software based on ICD-10-CM codes. The split-sample method was used to assess model overfitting and coefficient stability. The predictive power of the algorithms was ascertained by comparing the R, mean absolute percentage error, root mean square error, predictive ratios, and c-statistics. The expansion of the Clinical Classifications Software categories resulted in higher predictive power. The R reached 0.72 and 0.52 for the transformed and raw scale cost, respectively. The case-mix algorithm we developed based on age, sex, and diagnoses outperformed the existing case-mix models reported in the literature. The method developed in this study can be used by other health systems to produce tailored risk models for their specific purpose.

Healthcare Utilization and Infection in the Week Prior to Sepsis Hospitalization.

To quantify healthcare utilization in the week preceding sepsis hospitalization to identify potential opportunities to improve the recognition and treatment of sepsis prior to admission. Retrospective study. Two large integrated healthcare delivery systems in the United States. Hospitalized sepsis patients. None. We quantified clinician-based encounters in each of the 7 days preceding sepsis admission, as well as on the day of admission, and categorized them as: hospitalization, subacute nursing facility, emergency department, urgent care, primary care, and specialty care. We identified the proportion of encounters with diagnoses for acute infection based on 28 single-level Clinical Classification Software categories. We also quantified the use of antibiotics over the same interval and used linear regression to evaluate time trends. We included a total of 14,658 Kaiser Permanente Northern California sepsis hospitalizations and 31,369 Veterans Health Administration sepsis hospitalizations. Over 40% of patients in both cohorts required intensive care. A total of 7,747 Kaiser Permanente Northern California patients (52.9%) and 14,280 Veterans Health Administration patients (45.5%) were seen by a clinician in the week before sepsis. Prior to sepsis, utilization of subacute nursing facilities remained steady, whereas hospital utilization declined. Primary care, specialty care, and emergency department visits increased, particularly at admission day. Among those with a presepsis encounter, 2,648 Kaiser Permanente Northern California patients (34.2%) and 3,858 Veterans Health Administration patients (27.0%) had at least one acute infection diagnosis. An increasing percentage of outpatient encounters also had infectious diagnoses (3.3%/d; 95% CI, 1.5%-5.1%; p < 0.01), particularly in primary and specialty care settings. Prior to sepsis hospitalization, the use of antibiotics also increased steadily (2.1%/d; 95% CI, 1.1%-3.1%; p < 0.01). Over 45% of sepsis patients had clinician-based encounters in the week prior to hospitalization with an increasing frequency of diagnoses for acute infection and antibiotic use in the outpatient setting. These presepsis encounters offer several potential opportunities to improve the recognition, risk stratification, and treatment prior to sepsis hospitalization.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. 2012-2013 Truven MarketScan database. We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC+CCS diagnostic variables. Performance was evaluated based on cross-validated R2 and predictive ratios. Results show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas. Literature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs.

ICD-10-AM to CCS diagnostic codes: an exercise in clinical process mapping

Introduction: There is an increasing need for accurate hospital and health metrics in a bid to continue to improve the quality and delivery of health care. Using ICD-10-AM, the Irish health service has access to a diagnostic database that allows for richer and more flexible coding of conditions. Unfortunately, a rich database with 19,000 commonly reported diagnoses makes clinical coding and reporting more onerous. The Clinical Classification Software (CCS) collapses the 19,000 ICD codes into a more manageable 260 clinically more meaningful codes. Clinician feedback and intuitive study have shown a number of areas where we believe that there has been inappropriate mapping of ICD to CCS codes. Our aim was to identify any areas of clinical ambiguity, and to suggest changes on the basis of: a number of parameters (see Table 1). Method: In an attempt to identify possible changes that may be required to make the coding allocation more clinician friendly, we undertook a review of both ICD databases, the mapping algorithm provided by CCS, and the final CCS designations for ICD-10-AM diagnostic codes. Results: On initial study, we identified 1674 ICD1-10-AM codes for review. Of these, 1455 were believed to warrant re-coding, either to a different CCS code; a different CCS category; or a different clinical specialty (see below). Suggested changes based on: Accuracy: 2 changes Alcohol: 6 Anatomical site: 442 Cause: 437 Clinical specialty: 481 Sepsis: 7 Stroke: 61 Tuberculosis: 5 Residual: 14 Discussion: We determined that a little over 7% of the current ICD-10-AM codes could be recoded under a more clinical focus. Stroke is a special case in point. Different causes of stroke are treated differently, and this must be reflected in the coding. In partnership with the National Clinical Programme for Stroke, we have suggested re-mapping these codes to six new CCS groups. We have suggested that a number of other codes should be re-mapped using similar parameters Lessons Learned: We have developed a dialogue with the Healthcare Cost and Utilization Project (HCUP) in Washington, D.C., a division of the Agency of Healthcare Research and Quality. We have made suggestions on the basis of our findings and feedback from our clinical colleagues, and we hope that these suggestions can be added to future iterations of the CCS diagnostic codes and categories. Suggestions for future research: We believe that this is an example of inter-disciplinary and international co-operation in a bid to map together clinical codes in a manner which best represents the process flow and management protocols used to treat those conditions. Ultimately, this will make clinical coding more meaningful for the clinician, as well as providing increased accuracy and richness of hospital data. This will allow much greater access by clinicians to their own data in a manner that makes sense, and allows them to participate more fully in clinical audit and research and to drive service and quality improvement.

Epidemiology of patients presenting to the emergency centre of Princess Marina Hospital in Gaborone, Botswana

Introduction Emergency medicine is a newly recognized specialty in Botswana and the country launched an emergency medicine residency in January 2011. Data regarding the practice of emergency medicine in Botswana are limited. This study reviewed 1year of patient presentations to the emergency centre of Princess Marina Hospital, the country's main referral hospital located in the capital city, Gaborone. Methods Epidemiologic data of all patients presenting to the emergency centre between May 2010 and April 2011 were extracted into a database. The diagnoses of a random sample of patient presentations were coded using the categories outlined by the Clinical Classifications Software (CCS) for ICD-10. For ease of analysis, several CCS categories were grouped together for subsequent analysis. Results 24,905 patient encounters were recorded during the study period. A large proportion of patients were aged between 25 and 50years old. 20% of patients presented with a traumatic injury. The most common diagnoses across all ages included trauma, pregnancy complications, gastrointestinal disorders, and pneumonia. Conclusion These results can inform the development of emergency medicine education and acute care systems in Botswana.

Perioperative Complications and Mortality After Spinal Fusions

Retrospective review. To analyze the trends in complications and mortality after spinal fusions. Utilization of spinal fusions has been increasing during the past decade. It is essential to evaluate surgical outcomes to better identify patients who benefit most from surgical intervention. Integration of empiric evidence from large administrative databases into clinical decision making is instrumental in providing higher-quality, evidence-based, patient-centered care. This study used Nationwide Inpatient Sample data from 2001 through 2010. Patients who underwent spinal fusions were identified using the CCS (Clinical Classifications Software) and ICD-9 (International Classification of Diseases, 9th Revision) codes. Data on patient comorbidities, primary diagnosis, and postoperative complications were obtained via ICD-9 diagnosis codes and via CCS categories. National estimates were calculated using weights provided as part of the database. Time trend analysis for average length of stay, total charges, mortality, and comorbidity burden was performed. Univariate and multivariate models were constructed to identify predictors of mortality and postoperative complications. An estimated 3,552,873 spinal fusions were performed in the United States between 2001 and 2010. The national bill for spinal fusions increased from $10 billion to $46.8 billion. Today, patients are older and have a greater comorbidity burden than 10 years ago. Mortality remained relatively constant at 0.46%, 1.2%, and 0.14% for cervical, thoracic, and lumbar fusions, respectively. Morbidity rates showed an increasing trend at all levels. Multivariate analysis of 19 procedures and patient-related risk factors and 9 perioperative complications identified 85 statistically significant (P< 0.01) interactions. The data on perioperative risks and risk factors for postoperative complications of spinal fusions presented in this study is pivotal to appropriate surgical patient selection and well-informed risk-benefit evaluation of surgical intervention. N/A.

Use of State Discharge Abstract Data to Identify Hospitals Performing Similar Types of Operative Procedures

A hospital or anesthesia or surgical group that is making decisions to expand its practice benefits from knowledge of the types of surgical procedures performed at other hospitals. Discharge abstract data were used to study 1 hospital in each of 3 states. A similarity index provided information on which other hospitals in each state were competitors and/or peer institutions. To reveal potential growth opportunities, Clinical Classifications Software (CCS) grouping of specific types of procedures into broad categories was combined with the similarity index. The internal Herfindahl index was used to quantify the heterogeneity of procedures within each CCS category. Although the similarity index between many pairs of hospitals was highly influenced by the common orthopedic procedures performed at most hospitals, the similarity index was dominated by a large number of different types of procedures. For some pairs of hospitals, there was considerable overlap between the different types of procedures performed at each institution. For other pairs of hospitals, the overlap was small. Consequently, large hospitals were not always similar to each other because of the wide range of procedures performed. Smaller community hospitals were not always similar to each other either. Some small hospitals were sometimes similar to large metropolitan hospitals. The similarity index is a robust and valid method for quantitatively comparing the numbers and types of inpatient surgical procedures performed at different hospitals. The similarity index, when combined with CCS categories, is useful for identifying opportunities that enable surgeons and anesthesiologists to better meet the needs of their communities.

Admission to acute care hospitals for adolescent substance abuse: a national descriptive analysis

BackgroundUse of alcohol and illicit drugs by adolescents remains a problem in the U.S. Case identification and early treatment can occur within a broad variety of healthcare and non-healthcare settings, including acute care hospitals. The objective of this study is to describe the extent and nature of adolescent admissions to the acute inpatient setting for substance abuse (SA). We use the Agency for Healthcare Research and Quality (AHRQ) 2000 Healthcare Cost and Utilization Project Kids Inpatient Database (HCUP-KID) which includes over 2.5 million admissions for youth age 20 and under to 2,784 hospitals in 27 states in the year 2000. Specifically, this analysis estimates national number of admissions, mean total charges, and mean lengths of stay for adolescents between the ages of 12 and 17 admitted to an acute care hospital for the following diagnostic categories from the AHRQ's Clinical Classifications Software categories: "alcohol-related mental disorders" and "substance-related mental disorders". Frequency and percentage of total admissions were calculated for demographic variables of age, gender and income and for hospital characteristic variables of urban/rural designation and children's hospital designation.ResultsSA admissions represented 1.25 percent of adolescent admissions to acute care hospitals. Nearly 90 percent of the admission occurred in non-Children's hospitals. Most were for drug dependence (38%) or non-dependent use of alcohol or drugs (35%). Costs were highest for drug dependence admissions. Nearly half of admissions had comorbid mental health diagnoses. Higher rates of admission were seen in boys, in older adolescents, and in "self-pay" patients. Alcohol and drug rehabilitation/detoxification, alone or in combination with psychological and psychiatric evaluation and therapy, was documented for 38 percent of admissions. Over 50 percent of cases had no documentation of treatment specific to substance use behavior.ConclusionGeneral acute care hospitals have a significant and important opportunity to recognize, treat, and refer adolescents with substance abuse problems. These results suggest that inpatient facilities should develop and implement policies and processes to ensure that adolescent substance abusers admitted to their institutions receive appropriate care during the admission and appropriate referral to community care resources.