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Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

ABSTRACT: The optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established.Compare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes.We used 100% Texas Medicare data (2006-2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement (N=39,616). The ability of diagnosis-based [Charlson comorbidity score, Elixhauser comorbidity score, Combined Comorbidity Score, Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC)] versus prescription-based Chronic disease score in predicting 30-day mortality, 1-year mortality, 30-day readmission, complications, and FTR were compared using c-statistics (c) and integrated discrimination improvement (IDI).The overall 30-day mortality was 5.8%, 1-year mortality was 17.7%, 30-day readmission was 14.1%, complication rate was 39.7%, and FTR was 14.5%. CMS-HCC performed the best in predicting surgical outcomes (30-d mortality, c=0.797, IDI=4.59%; 1-y mortality, c=0.798, IDI=9.60%; 30-d readmission, c=0.630, IDI=1.27%; complications, c=0.766, IDI=9.37%; FTR, c=0.811, IDI=5.24%) followed by Elixhauser comorbidity index/disease categories (30-d mortality, c=0.750, IDI=2.37%; 1-y mortality, c=0.755, IDI=5.82%; 30-d readmission, c=0.629, IDI=1.43%; complications, c=0.730, IDI=3.99%; FTR, c=0.749, IDI=2.17%). Addition of prescription-based scores to diagnosis-based scores did not improve performance.The CMS-HCC had superior performance in predicting surgical outcomes. Prescription-based scores, alone or in addition to diagnosis-based scores, were not better than any diagnosis-based scoring system.

SUBMITTER: Mehta HB

PROVIDER: S-EPMC4713334 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

Mehta Hemalkumar B HB Dimou Francesca F Adhikari Deepak D Tamirisa Nina P NP Sieloff Eric E Williams Taylor P TP Kuo Yong-Fang YF Riall Taylor S TS

Medical care 20160201 2

<h4>Introduction</h4>The optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established.<h4>Objective</h4>Compare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes.<h4>Methods</h4>We used 100% Texas Medicare data (2006-2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovasc ...[more]

PMID: 26595225

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Project description:There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient's HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors' predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

Project description:ObjectiveThe concept of the 'self-fulfilling prophecy' is well established in intracerebral haemorrhage (ICH). The ability to improve prognostication and prediction of long-term outcomes during the first days of hospitalisation is important in guiding conversations around goals of care. We previously demonstrated that incorporating delayed imaging into various prognostication scores for ICH improves the predictive accuracy of 90-day mortality. However, delayed prognostication scores have not been used to predict long-term functional outcomes beyond 90 days.Design, setting and participantsWe analysed data from the ICH Deferoxamine trial to see if delaying the use of prognostication scores to 96 hours after ICH onset will improve performance to predict outcomes at 180 days. 276 patients were included.Interventions and measurementsWe calculated the original ICH score (oICH), modified-ICH score (MICH), max-ICH score and the FUNC score on presentation (baseline), and on day 4 (delayed). Outcomes assessed were mortality and poor functional outcome in survivors (defined as modified Rankin Scale of 4-5) at 180 days. We generated receiver operating characteristic curves, and measured the area under the curve values (AUC) for mortality and functional outcome. We compared baseline and delayed AUCs with non-parametric methods.ResultsAt 180 days, 21 of 276 (7.6%) died. Out of the survivors, 54 of 255 had poor functional outcome (21.2%). The oICH, MICH and max-ICH performed significantly better at predicting 180-day mortality when calculated 4 days later compared with their baseline equivalents ((0.74 vs 0.83, p=0.005), (0.73 vs 0.80, p=0.036), (0.74 vs 0.83, p=0.008), respectively). The delayed calculation of these scores did not significantly improve our accuracy for predicting poor functional outcomes.ConclusionDelaying the calculation of prognostication scores in acute ICH until day 4 improved prediction of 6-month mortality but not functional outcomes.Trial registration numberClinicalTrials.gov Registry (NCT02175225).

Project description:The protective effect of different healthy lifestyle scores for the risk of cardiovascular disease (CVD) was reported, although the comparisons of performance were lacking. We compared the performance measures of CVDs from different healthy lifestyle scores among Taiwanese adults. We conducted a nationwide prospective cohort study of 6042 participants (median age 43 years, 50.2% women) in Taiwan’s Hypertensive, Hyperglycemia and Hyperlipidemia Survey, of whom 2002 were free of CVD at baseline. The simple and weighted the Mediterranean diet related healthy lifestyle (MHL) scores were defined as a combination of normal body mass index, Mediterranean diet, adequate physical activity, non-smokers, regular healthy drinking, and each dichotomous lifestyle factor. The World Cancer Research Fund and American Institute for Cancer Research cancer prevention recommended lifestyle and Life's Simple 7 following the guideline definition. The incidence of CVD among the four healthy lifestyle scores, each divided into four subgroups, was estimated. During a median 14.3 years follow-up period, 520 cases developed CVD. In the multivariate-adjusted Cox proportional hazard models, adherence to the highest category compared with the lowest one was associated with a lower incidence of CVD events, based on the simple (hazard ratio [HR] 0.43, 95% confidence interval [CI] 0.2–0.94) and weighted MHL scores (HR 0.44, 95% CI 0.28–0.68). Additionally, age played a role as a significant effect modifier for the protective effect of the healthy lifestyle scores for CVD risk. Specifically, the performance measures by integrated discriminative improvement showed a significant increase after adding the simple MHL score (integrated discriminative improvement: 0.51, 95% CI 0.16–0.86, P = 0.002) and weighted MHL score (integrated discriminative improvement: 0.38, 95% CI 0.01–0.74, P = 0.021). We demonstrated that the healthy lifestyle scores with an inverse association with CVD and reduced CVD risk were more likely for young adults than for old adults. Further studies to study the mechanism of the role of lifestyle on CVD prevention are warranted.

Project description:Purpose:Multiple studies have suggested that comorbidities pose negative impacts on the survival of patients with chronic obstructive pulmonary disease (COPD); few have applied comorbidity measures driven from health insurance claims databases to predict various health outcomes. We aimed to examine the performance of commonly used comorbidity measures based on diagnosis and pharmacy dispensing claims information in predicting future death and hospitalization in COPD patients. Methods:We identified COPD patients in a population-based Taiwanese database. We built logistic regression models with age, sex, and baseline comorbidities measured by either diagnosis or pharmacy claims information as predictors of subsequent-year death or hospitalization in a random 50% sample and validated the discrimination in the other 50%. The diagnosis-based comorbidity measures included the Charlson Index and the Elixhauser comorbidity measure; the pharmacy-based comorbidity measures included the updated Chronic Disease Score (CDS) and the Pharmacy-Based Comorbidity Index (PBDI). Results:We identified 428,251 eligible patients. For overall death, the Elixhauser comorbidity measure showed the best predictive performance (c-statistic=0.832), followed by the PBDI (c-statistic=0.822), the Charlson Index (c-statistic=0.815), and the updated CDS (c-statistic=0.808). For overall hospitalization, the PBDI (c-statistics=0.730) and the Elixhauser comorbidity measure (c-statistics=0.724) outperformed the updated CDS (c-statistics=0.714) and the Charlson Index (c-statistics=0.710). For hospitalization due to cardiovascular, cerebrovascular, or respiratory diseases, the comorbidity models showed similar predictive ranks and demonstrated c-statistics higher than 0.75. However, none of the models could adequately predict hospitalization due to other reasons (c-statistics < 0.60). Conclusion:Our study comprehensively compared the predictive performance of comorbidity measures. The Elixhauser comorbidity measure and the PBDI are useful tools for describing comorbid conditions and predicting health outcomes in COPD patients.

Project description:BackgroundIt is important to find a comorbidity measure with better performance for use with administrative data. The new method proposed by Elixhauser et al. has never been validated and compared to the widely used Charlson method in the Asia region. The objective of this study was to compare the performance of three comorbidity measures using information from different data periods in predicting short- and long-term mortality among patients with acute myocardial infarction (AMI) and chronic obstructive pulmonary disease (COPD).MethodsWe conducted a retrospective cohort study using National Health Insurance claims data (2001-2002) in Taiwan. We constructed the Elixhauser, the Charlson/Deyo, and the Charlson/Romano methods based on the International Classification of Disease, 9th Revision, Clinical Modification codes in the claims data. Two data periods, including the index hospitalization as well as the index and prior 1-year hospitalizations, were used in the analysis. The performances were compared using the c-statistics derived from multiple logistic regression models that included age, gender, race, and whether the patient received surgery or not. The outcomes of interest were in-hospital and 1-year mortality.ResultsThe performance was in the same rank order among both populations regardless of the outcome and data period: Elixhauser > Charlson/Romano > Charlson/Deyo. In predicting in-hospital mortality, the Elixhauser models using information from the index hospitalization performed best, even better than the Charlson/Deyo or Charlson/Romano models using information from the index and prior hospitalizations. Nevertheless, in predicting 1-year mortality, the Elixhauser models using information from the index and 1-year prior hospitalizations performed better than using information from the index hospitalization only.ConclusionsThis is so far the first study to validate the Elixhauser method and compare it to other methods in the Asia region, and is the first to report its differences in data periods between short- and long-term outcomes. The comorbidity measurement developed by Elixhauser et al. has relatively good predictive validity, and researchers should consider its use in claims-based studies.

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Dataset Information

Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

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Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

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