Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data.
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ABSTRACT:
SUBMITTER: Nimptsch U
PROVIDER: S-EPMC4874831 | biostudies-other | 2016 Jun
REPOSITORIES: biostudies-other
Publications
Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data.
Health services research 20151007 3
<h4>Objective</h4>To investigate changes in comorbidity coding after the introduction of diagnosis related groups (DRGs) based prospective payment and whether trends differ regarding specific comorbidities.<h4>Data sources</h4>Nationwide administrative data (DRG statistics) from German acute care hospitals from 2005 to 2012.<h4>Study design</h4>Observational study to analyze trends in comorbidity coding in patients hospitalized for common primary diseases and the effects on comorbidity-related r ...[more]
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Project description:BackgroundComorbid condition and hospital risk-adjusted outcomes prevalence were compared based on clinical registry vs administrative claims data.HypothesisRisk-adjusted outcomes will vary depending on the source of comorbidity data used.MethodsClinical data from hospitalized Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the American College of Cardiology/American Heart Association (ACC/AHA) Guidelines (CRUSADE) non-ST-segment elevation myocardial infarction (NSTEMI) patients ≥65 years was linked to Medicare claims. Eight common comorbid conditions were coded and compared between registry data (derived from medical record review) and claims data; hospital-level observed vs expected ratios and outlier status for 30-day readmission and mortality were calculated using logistic generalized estimating equations for clinical vs claims data.ResultsOf 68 199 NSTEMI patients, 48.1% were female, 86.9% were white, and median age was 78. Degree of agreement between data sources for comorbid condition prevalence was 67.8% for myocardial infarction and 89.3% for diabetes. Overall, multivariable model performance was similar: Medicare mortality c-statistics is 0.69 vs CRUSADE is 0.71; readmission c-statistics is 0.59 for both. Hospital ratings were similar regardless of data source (mortality, R2 = 0.97863; readmission, R2 = 0.97858). Eighty-two hospitals were mortality outliers in claims-based models; of these, 70 were outliers in registry-based models. Forty-five hospitals were readmission outliers in claims-based models; of these, 39 were outliers in registry-based models.ConclusionsThere were significant differences in individual comorbid condition prevalence when derived from registries vs claims, but hospital-level outcomes were comparable.
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Project description:BackgroundOlder adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes.ObjectiveWe aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms.MethodsThis was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy.ResultsIn total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease.ConclusionsThe physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.
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Project description:BackgroundWhile mental comorbidity is considered common in multiple sclerosis (MS), its impact is poorly defined; methods are needed to support studies of mental comorbidity. We validated and applied administrative case definitions for any mental comorbidities in MS.MethodsUsing administrative health data we identified persons with MS and a matched general population cohort. Administrative case definitions for any mental comorbidity, any mood disorder, depression, anxiety, bipolar disorder and schizophrenia were developed and validated against medical records using a a kappa statistic (k). Using these definitions we estimated the prevalence of these comorbidities in the study populations.ResultsCompared to medical records, administrative definitions showed moderate agreement for any mental comorbidity, mood disorders and depression (all k ? 0.49), fair agreement for anxiety (k = 0.23) and bipolar disorder (k = 0.30), and near perfect agreement for schizophrenia (k = 1.0). The age-standardized prevalence of all mental comorbidities was higher in the MS than in the general populations: depression (31.7% vs. 20.5%), anxiety (35.6% vs. 29.6%), and bipolar disorder (5.83% vs. 3.45%), except for schizophrenia (0.93% vs. 0.93%).ConclusionsAdministrative data are a valid means of surveillance of mental comorbidity in MS. The prevalence of mental comorbidities, except schizophrenia, is increased in MS compared to the general population.
| S-EPMC3599013 | biostudies-literature
Project description:BACKGROUND:Research investigating risk factors for hospital-acquired pressure injury (HAPI) has primarily focused on the characteristics of patients and nursing staff. Limited data are available on the association of hospital characteristics with HAPI. OBJECTIVE:We aimed to quantify the association of hospital characteristics with HAPI and their effect on residual hospital variation in HAPI risk. METHODS:We employed a retrospective cohort study design with split validation using hierarchical survival analysis. This study extends the analysis "Hospital-Acquired Pressure Injury (HAPI): Risk Adjusted Comparisons in an Integrated Healthcare Delivery System" by Rondinelli et al. (2018) to include hospital-level factors. We analyzed 1,661 HAPI episodes among 728,266 adult hospitalization episodes across 35 California Kaiser Permanente hospitals, an integrated healthcare delivery system between January 1, 2013, and June 30, 2015. RESULTS:After adjusting for patient-level and hospital-level variables, 2 out of 12 candidate hospital variables were statistically significant predictors of HAPI. The hazard for HAPI decreased by 4.8% for every 0.1% increase in a hospital's mean mortality ([6.3%, 2.6%], p < .001), whereas every 1% increase in a hospital's proportion of patients with a history of diabetes increased HAPI hazard by 5% ([-0.04%, 10.0%], p = .072). Addition of these hierarchical variables decreased unexplained hospital variation of HAPI risk by 35%. DISCUSSION:We found hospitals with higher patient mortality had lower HAPI risk. Higher patient mortality may decrease the pool of patients who live to HAPI occurrence. Such hospitals may also provide more resources (specialty staff) to care for frail patient populations. Future research should aim to combine hospital data sets to overcome power limitations at the hospital level and should investigate additional measures of structure and process related to HAPI care.
| S-EPMC6023742 | biostudies-literature
Project description:BACKGROUND:Healthcare-associated infections impose a significant burden on the healthcare system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times and imperfect accuracy, reducing their effectiveness. METHODS:This study examined whether the amount of time a patient spends on a ward with other patients clinically suspected of infection, termed 'co-presence', can be used as a tool to predict subsequent healthcare-associated infection. Compared with contact tracing, this leverages passively collected electronic data rather than manually collected data, allowing for improved monitoring. All 133,304 inpatient records between 2011 and 2015 were abstracted from a healthcare system in the UK. The area under the receiver-operator curve (AUROC) for each of five pathogens was calculated based on co-presence time, sensitivity and specificity of the test, and how much earlier co-presence would have predicted infection for the true-positive cases. FINDINGS:For the five pathogens, AUROC ranged from 0.92 to 0.99, and was 0.52 for the negative control. Optimal cut-points of co-presence ranged from 25 to 59 h, and would have led to detection of true-positive cases up to an average of 1 day earlier. INTERPRETATION:These findings show that co-presence time would help to predict healthcare-acquired infection, and would do so earlier than the current standard of care. Using this measure prospectively in hospitals based on real-time data could limit the consequences of infection, both by being able to treat individual infected patients earlier, and by preventing potential secondary infections stemming from the original infected patient.
| S-EPMC7395302 | biostudies-literature
Project description:OBJECTIVE: To develop a tool for estimating hospital-specific inpatient prices for major payers. DATA SOURCES: AHRQ Healthcare Cost and Utilization Project State Inpatient Databases and complete hospital financial reporting of revenues mandated in 10 states for 2006. STUDY DESIGN: Hospital discharge records and hospital financial information were merged to estimate revenue per stay by payer. Estimated prices were validated against other data sources. PRINCIPAL FINDINGS: Hospital prices can be reasonably estimated for 10 geographically diverse states. All-payer price-to-charge ratios, an intermediate step in estimating prices, compare favorably to cost-to-charge ratios. Estimated prices also compare well with Medicare, MarketScan private insurance, and the Medical Expenditure Panel Survey prices for major payers, given limitations of each dataset. CONCLUSIONS: Public reporting of prices is a consumer resource in making decisions about health care treatment; for self-pay patients, they can provide leverage in negotiating discounts off of charges. Researchers can also use prices to increase understanding of the level and causes of price differentials among geographic areas. Prices by payer expand investigational tools available to study the interaction of inpatient hospital price setting among public and private payers--an important asset as the payer mix changes with the implementation of the Affordable Care Act.
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Project description:Adjustment for the differing risk profiles of patients is essential to the use of administrative hospital data for epidemiological research. Smoking is an important factor to include in such adjustments, but the accuracy of the diagnostic codes denoting smoking in hospital records is unknown. The aims of this study were to measure the validity of current smoking and ever smoked status identified from diagnoses in hospital records using a range of algorithms, relative to self-reported smoking status; and to examine whether the misclassification of smoking identified through hospital data is differential or non-differential with respect to common exposures and outcomes. Data from the baseline questionnaire of the 45 and Up Study, completed by 267,153 residents of New South Wales (NSW), Australia, aged 45 years and older, were linked to the NSW Admitted Patient Data Collection. Patients who had been admitted to hospital for an overnight stay between 1 July 2005 and the date of completion of the questionnaire (1 January 2006 to 2 March 2009) were included. Smokers were identified by applying a range of algorithms to hospital admission histories, and compared against self-reported smoking in the questionnaire ('gold standard'). Sensitivities for current smoking ranged from 59% to 84%, while specificities were 94% to 98%. Sensitivities for ever smoked ranged from 45% to 74% and specificities were 93% to 97%. For the majority of algorithms, sensitivities and/or specificities differed significantly according to principal diagnosis, number of comorbidities, socioeconomic status, residential remoteness, Indigenous status, 28 day readmission and 365 day mortality. The identification of smoking through diagnoses in hospital data results in differential misclassification. Risk adjustment based on smoking identified from these data will yield potentially misleading results. Systematic capture of information about smoking in hospital records using a mandatory item would increase the utility of administrative data for epidemiological research.
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Project description:ContextMany of the publicly available health quality report cards are based on administrative data. ICD-9-CM codes in administrative data are not date stamped to distinguish between medical conditions present at the time of hospital admission and complications, which occur after hospital admission. Treating complications as preexisting conditions gives poor-performing hospitals "credit" for their complications and may cause some hospitals that are delivering low-quality care to be misclassified as average- or high-performing hospitals.ObjectiveTo determine whether hospital quality assessment based on administrative data is impacted by the inclusion of condition present at admission (CPAA) modifiers in administrative data as a date stamp indicator.Design, setting, and patientsRetrospective cohort study based on 648,866 inpatient admissions between 1998 and 2000 for coronary artery bypass graft (CABG) surgery, coronary angioplasty (PTCA), carotid endarterectomy (CEA), abdominal aortic aneurysm (AAA) repair, total hip replacement (THR), acute MI (AMI), and stroke using the California State Inpatient Database which includes CPAA modifiers. Hierarchical logistic regression was used to create separate condition-specific risk adjustment models. For each study population, one model was constructed using only secondary diagnoses present at admission based on the CPAA modifier: "date stamp" model. The second model was constructed using all secondary diagnoses, ignoring the information present in the CPAA modifier: the "no date stamp model." Hospital quality was assessed separately using the "date stamp" and the "no date stamp" risk-adjustment models.ResultsForty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 40 percent of the THR hospitals, and 33 percent of the AMI hospitals identified as low-performance hospitals by the "date stamp" models were not classified as low-performance hospitals by the "no date stamp" models. Fifty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 50 percent of the CEA hospitals, and 36 percent of the AMI hospitals identified as low-performance hospitals by the "no date stamp" models were not identified as low-performance hospitals by the "date stamp" models. The inclusion of the CPAA modifier had a minor impact on hospital quality assessment for AAA repair, stroke, and CEA.ConclusionThis study supports the hypothesis that the use of routine administrative data without date stamp information to construct hospital quality report cards may result in the mis-identification of hospital quality outliers. However, the CPAA modifier will need to be further validated before date stamped administrative data can be used as the basis for health quality report cards.
| S-EPMC1797077 | biostudies-literature
Project description:BackgroundAnalysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved.MethodsThis was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased).Findings91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30·8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1·457 [1·408-1·509]), greater deprivation (1·002 [1·001-1·003]), Asian (1·211 [1·128-1·299]) or mixed ethnicity (1·317 [1·080-1·605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5·433 [4·618-6·392]). Later date of discharge was associated with a lower odds of death (0·977 [0·976-0·978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52·2% in the first week of March to 16·8% in the last week of May.InterpretationReductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours.FundingNone.
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Project description:Importance:Current surgical quality metrics might be insufficient to fully judge the quality of certain operations because they are not procedure specific. Hypocalcemia, recurrent laryngeal nerve (RLN) injury, and hematoma are considered to be the most relevant outcomes to measure after thyroidectomy. Whether these outcomes can be used as hospital quality metrics is unknown. Objectives:To evaluate whether thyroidectomy-specific outcomes vary among hospitals, whether the addition of thyroidectomy-specific variables affects risk adjustment, and whether differences in hospital performance are associated with thyroidectomy-specific care processes. Design, Setting, and Participants:In this retrospective cohort study, patients undergoing thyroidectomies from January 1, 2013, through December 31, 2015, at hospitals participating in the American College of Surgeons' National Surgical Quality Improvement Program were studied. Exposure:Thyroidectomy-related care. Main Outcomes and Measures:Clinically severe hypocalcemia, RLN injury, and clinically significant hematoma within 30 days of thyroid surgery and hospital-level performance variation, change in risk adjustment, and association with processes. Results:Overall, 14 540 patients (mean [SD] age, 52.1 [15.0] years; 11 499 [79.1%] female) underwent operations at 98 hospitals. Because operations missing thyroidectomy-specific outcomes were excluded, the numbers of operations and hospitals analyzed differed by outcome. Of 14 540 operations included, clinically severe hypocalcemia occurred in 450 patients (3.3% overall, 0.6% after partial, and 4.7% after subtotal or total thyroidectomy), RLN injury in 755 patients (5.7% overall, 4.2% after partial, and 6.6% after subtotal or total thyroidectomy), and hematoma in 175 patients (1.3%). Hospital performance varied for hypocalcemia and RLN injury but not for hematoma. Hospital performance rankings were largely unaffected by the inclusion of thyroidectomy-specific data in risk adjustment. With regard to processes, patients undergoing thyroidectomies at the best-performing vs worst-performing hospitals less frequently had their postoperative parathyroid hormone level measured (593 [19.9%] vs 457 [31.7%], P < .001) and more often were prescribed oral calcium, vitamin D, or both (2281 [76.6%] vs 962 [66.8%], P < .001). When profiled by RLN injury, use of energy devices (1517 [69.1%] vs 507 [55.2%], P < .001) and intraoperative nerve monitoring (1223 [55.7%] vs 346 [37.7%], P < .001) were more prevalent at the best- compared with the worst-performing hospitals. Conclusions and Relevance:Postoperative hypocalcemia and RLN injury, but not hematoma, potentially could be used as thyroidectomy-specific national hospital quality improvement metrics. Strategies aimed at reducing these complications after thyroidectomy may improve the care of these patients.
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