Stroke Administrative Severity Index: using administrative data for 30-day poststroke outcomes prediction.
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ABSTRACT:
SUBMITTER: Simpson AN
PROVIDER: S-EPMC6615407 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
Publications
Stroke Administrative Severity Index: using administrative data for 30-day poststroke outcomes prediction.
Simpson Annie N AN Wilmskoetter Janina J Hong Ickpyo I Li Chih-Ying CY Jauch Edward C EC Bonilha Heather S HS Anderson Kelly K Harvey Jillian J Simpson Kit N KN
Journal of comparative effectiveness research 20171023 4
<h4>Aim</h4>Current stroke severity scales cannot be used for archival data. We develop and validate a measure of stroke severity at hospital discharge (Stroke Administrative Severity Index [SASI]) for use in billing data.<h4>Methods</h4>We used the NIH Stroke Scale (NIHSS) as the theoretical framework and identified 285 relevant International Classification of Diseases, 9th Revision diagnosis and procedure codes, grouping them into 23 indicator variables using cluster analysis. A 60% sample of ...[more]
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Project description:AimsThe LE index (Length of hospitalization plus number of Emergent visits ≤6 months) predicts 30 day all-cause readmission or death following hospitalization for heart failure (HF). We combined N-terminal pro-B type natriuretic peptide (NT-proBNP) levels with the LE index to derive and validate the LENT index for risk prediction at the point of care on the day of hospital discharge.Methods and resultsIn this prospective cohort sub-study of the Patient-centred Care Transitions in HF clinical trial, we used log-binomial regression models with LE index and either admission or discharge NT-proBNP as the predictors and 30 day composite all-cause readmission or death as the primary outcome. No other variables were added to the model. We used regression coefficients to derive the LENT index and bootstrapping analysis for internal validation. There were 772 patients (mean [SD] age 77.0 [12.4] years, 49.9% female). Each increment in the LE index was associated with a 25% increased risk of the primary outcome (RR 1.25, 95% CI 1.16-1.35; C-statistic 0.63). Adjusted for the LE index, every 10-fold increase in admission and discharge NT-proBNP was associated with a 48% (RR 1.48; 95% CI 1.10, 1.99; C-statistic 0.64; net reclassification index [NRI] 0.19) and 56% (RR 1.56; 95% CI 1.08, 2.25; C-statistic 0.64; NRI 0.21) increased risk of the primary outcome, respectively. The predicted probability of the primary outcome increased to a similar extent with incremental LENT, regardless of whether admission or discharge NT-proBNP level was used.ConclusionsThe point-of-care LENT index predicts 30 day composite all-cause readmission or death among patients hospitalized with HF, with improved risk reclassification compared with the LE index. The performance of this simple, 3-variable index - without adjustment for comorbidities - is comparable to complex risk prediction models in HF.
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Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.
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Project description:INTRODUCTION:There are currently no robust tools available for risk stratification of emergency department (ED) patients with lower gastrointestinal bleed (LGIB). Our aim was to identify risk factors and develop a preliminary model to predict 30-day serious adverse events among ED LGIB patients. METHODS:We conducted a health records review including adult ED patients with acute LGIB. We used a composite outcome of 30-day all-cause death, recurrent LGIB, need for intervention to control the bleeding, and severe adverse events resulting in intensive care unit admission. One researcher collected data for variables and a second researcher independently collected 10% of the variables for inter-observer reliability. We used backward multivariable logistic regression analysis and SELECTION=SCORE option to create a preliminary risk-stratification tool. We assessed the diagnostic accuracy of the final model. RESULTS:Of 372 patients, 48 experienced an adverse outcome. We found that age ≥75 years, hemoglobin ≤100 g/L, international normalized ratio ≥2.0, ongoing bleed in the ED, and a medical history of colorectal polyps were statistically significant predictors in the multivariable regression analysis. The area under the curve (AUC) for the model was 0.83 (95% confidence interval, 0.77-0.89). We developed a scoring system based on the logistic regression model and found a sensitivity 0.96 (0.90-1.00) and specificity 0.53 (0.48-0.59) for a cut-off score of 1. CONCLUSION:This model showed good ability to differentiate patients with and without serious outcomes as evidenced by the high AUC and sensitivity. The results of this study could be used in the prospective derivation of a clinical decision tool.
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Project description:Background and Purpose- Kidney dysfunction is common among patients hospitalized for ischemic stroke. Understanding the association of kidney disease with poststroke outcomes is important to properly adjust for case mix in outcome studies, payment models and risk-standardized hospital readmission rates. Methods- In this cohort study of fee-for-service Medicare patients admitted with ischemic stroke to 1579 Get With The Guidelines-Stroke participating hospitals between 2009 and 2014, adjusted multivariable Cox proportional hazards models were used to determine the independent associations of estimated glomerular filtration rate (eGFR) and dialysis status with 30-day and 1-year postdischarge mortality and rehospitalizations. Results- Of 204?652 patients discharged alive (median age [25th-75th percentile] 80 years [73.0-86.0], 57.6% women, 79.8% white), 48.8% had an eGFR ?60, 26.5% an eGFR 45 to 59, 16.3% an eGFR 30 to 44, 5.1% an eGFR 15 to 29, 0.6% an eGFR <15 without dialysis, and 2.8% were receiving dialysis. Compared with eGFR ?60, and after adjusting for relevant variables, eGFR <45 was associated with increased 30-day mortality with the risk highest among those with eGFR <15 without dialysis (hazard ratio [HR], 2.09; 95% CI, 1.66-2.63). An eGFR <60 was associated with increased 1-year poststroke mortality that was highest among patients on dialysis (HR, 2.65; 95% CI, 2.49-2.81). Dialysis was also associated with the highest 30-day and 1-year rehospitalization rates (HR, 2.10; 95% CI, 1.95-2.26 and HR, 2.55; 95% CI, 2.44-2.66, respectively) and 30-day and 1-year composite of mortality and rehospitalization (HR, 2.04; 95% CI, 1.90-2.18 and HR, 2.46; 95% CI, 2.36-2.56, respectively). Conclusions- Within the first year after index hospitalization for ischemic stroke, eGFR and dialysis status on admission are associated with poststroke mortality and hospital readmissions. Kidney function should be included in risk-stratification models for poststroke outcomes.
| S-EPMC6338440 | biostudies-literature
Project description:ObjectiveDelirium is associated with poor outcomes among critically ill patients. However, it is not well characterized among patients with ischemic or hemorrhagic stroke (IS and HS). We provide the population-level frequency of in-hospital delirium and assess its association with in-hospital outcomes and with 30-day readmission among IS and HS patients.MethodsWe analyzed Nationwide in-hospital and readmission data for years 2010-2015 and identified stroke patients using ICD-9 codes. Delirium was identified using validated algorithms. Outcomes were in-hospital mortality, length of stay, unfavorable discharge disposition, and 30-day readmission. We used survey design logistic regression methods to provide national estimates of proportions and 95% confidence intervals (CI) for delirium, and odds ratios (OR) for association between delirium and poor outcomes.ResultsWe identified 3,107,437 stroke discharges of whom 7.45% were coded to have delirium. This proportion significantly increased between 2010 (6.3%) and 2015 (8.7%) (aOR, 95% CI: 1.04, 1.03-1.05). Delirium proportion was higher among HS patients (ICH: 10.0%, SAH: 9.8%) as compared to IS patients (7.0%). Delirious stroke patients had higher in-hospital mortality (12.3% vs. 7.8%), longer in-hospital stay (11.6 days vs. 7.3 days) and a significantly greater adjusted risk of 30-day-readmission (16.7%) as compared to those without delirium (12.2%) (aRR, 95% CI: 1.13, 1.11-1.15). Upon readmission, patients with delirium at initial admission continued to have a longer length of stay (7.7 days vs. 6.6 days) and a higher in-hospital mortality (9.3% vs. 6.4%).ConclusionDelirium identified through claims data in stroke patients is independently associated with poor in-hospital outcomes both at index admission and readmission. Identification and management of delirium among stroke patients provides an opportunity to improve outcomes.
| S-EPMC6855446 | biostudies-literature
Project description:BackgroundThe LACE index-length of stay (L), acuity (A), Charlson co-morbidities (C), and emergent visits (E)-predicts 30-day outcomes following heart failure (HF) hospitalization but is complex to score. A simpler LE index (length of stay and emergent visits) could offer a practical advantage in point-of-care risk prediction.Methods and resultsThis was a sub-study of the patient-centred care transitions in HF (PACT-HF) multicentre trial. The derivation cohort comprised patients hospitalized for HF, enrolled in the trial, and followed prospectively. External validation was performed retrospectively in a cohort of patients hospitalized for HF. We used log-binomial regression models with LACE or LE as the predictor and either 30-day composite all-cause readmission or death or 30-day all-cause readmission as the outcomes, adjusting only for post-discharge services. There were 1985 patients (mean [SD] age 78.1 [12.1] years) in the derivation cohort and 378 (mean [SD] age 73.1 [13.2] years) in the validation cohort. Increments in the LACE and LE indices were associated with 17% (RR 1.17; 95% CI 1.12, 1.21; C-statistic 0.64) and 21% (RR 1.21; 95% CI 1.15, 1.26; C-statistic 0.63) increases, respectively, in 30-day composite all-cause readmission or death; and 16% (RR 1.16; 95% CI 1.11, 1.20; C-statistic 0.64) and 18% (RR 1.18; 95% CI 1.13, 1.24; C-statistic 0.62) increases, respectively, in 30-day all-cause readmission. The LE index provided better risk discrimination for the 30-day outcomes than did the LACE index in the external validation cohort.ConclusionsThe LE index predicts 30-day outcomes following HF hospitalization with similar or better performance than the more complex LACE index.
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Project description:BackgroundOutcome measures for patients hospitalized with pneumonia may complement process measures in characterizing quality of care. We sought to develop and validate a hierarchical regression model using Medicare claims data that produces hospital-level, risk-standardized 30-day mortality rates useful for public reporting for patients hospitalized with pneumonia.Methodology/principal findingsRetrospective study of fee-for-service Medicare beneficiaries age 66 years and older with a principal discharge diagnosis of pneumonia. Candidate risk-adjustment variables included patient demographics, administrative diagnosis codes from the index hospitalization, and all inpatient and outpatient encounters from the year before admission. The model derivation cohort included 224,608 pneumonia cases admitted to 4,664 hospitals in 2000, and validation cohorts included cases from each of years 1998-2003. We compared model-derived state-level standardized mortality estimates with medical record-derived state-level standardized mortality estimates using data from the Medicare National Pneumonia Project on 50,858 patients hospitalized from 1998-2001. The final model included 31 variables and had an area under the Receiver Operating Characteristic curve of 0.72. In each administrative claims validation cohort, model fit was similar to the derivation cohort. The distribution of standardized mortality rates among hospitals ranged from 13.0% to 23.7%, with 25(th), 50(th), and 75(th) percentiles of 16.5%, 17.4%, and 18.3%, respectively. Comparing model-derived risk-standardized state mortality rates with medical record-derived estimates, the correlation coefficient was 0.86 (Standard Error?=?0.032).Conclusions/significanceAn administrative claims-based model for profiling hospitals for pneumonia mortality performs consistently over several years and produces hospital estimates close to those using a medical record model.
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