Effect of Present-on-Admission (POA) Reporting Accuracy on Hospital Performance Assessments Using Risk-Adjusted Mortality.
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ABSTRACT: Objective
To evaluate how the accuracy of present-on-admission (POA) reporting affects hospital 30-day acute myocardial infarction (AMI) mortality assessments.Data sources
A total of 2005 California patient discharge data (PDD) and vital statistics death files.Study design
We compared hospital performance rankings using an established model assessing hospital performance for AMI with (1) a model incorporating POA indicators of whether a secondary condition was a comorbidity or a complication of care, and (2) a simulation analysis that factored POA indicator accuracy into the hospital performance assessment. For each simulation, we changed POA indicators for six major acute risk factors of AMI mortality. The probability of POA being changed depended on patient and hospital characteristics.Principal findings
Comparing the performance rankings of 268 hospitals using the established model with that using the POA indicator, 67 hospitals' (25 percent) rank differed by ≥10 percent. POA reporting inaccuracy due to overreporting and underreporting had little additional impact; POA overreporting contributed to 4 percent of hospitals' difference in rank compared to the POA model and POA underreporting contributed to <1 percent difference.Conclusion
Incorporating POA indicators into risk-adjusted models of AMI care has a substantial impact on hospital rankings of performance that is not primarily attributable to inaccuracy in POA hospital reporting.
SUBMITTER: Goldman LE
PROVIDER: S-EPMC4450937 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo test the accuracy of reporting present-on-admission (POA) and to assess whether POA reporting accuracy differs by hospital characteristics.Data sourcesWe performed an audit of POA reporting of secondary diagnoses in 1,059 medical records from 48 California hospitals.Study designWe used patient discharge data (PDD) to select records with secondary diagnoses that are powerful predictors of mortality and could potentially represent comorbidities or complications among patients who either had a primary procedure of a percutaneous transluminal coronary angioplasty or a primary diagnosis of acute myocardial infarction, community-acquired pneumonia, or congestive heart failure. We modeled the relationship between secondary diagnoses POA reporting accuracy (over-reporting and under-reporting) and hospital characteristics.Data collectionWe created a gold standard from blind reabstraction of the medical records and compared the accuracy of the PDD against the gold standard.Principal findingsThe PDD and gold standard agreed on POA reporting in 74.3 percent of records, with 13.7 percent over-reporting and 11.9 percent under-reporting. For-profit hospitals tended to overcode secondary diagnoses as present on admission (odds ratios [OR] 1.96; 95 percent confidence interval [CI] 1.11, 3.44), whereas teaching hospitals tended to undercode secondary diagnoses as present on admission (OR 2.61; 95 percent CI 1.36, 5.03).ConclusionsPOA reporting of secondary diagnoses is moderately accurate but varies by hospitals. Steps should be taken to improve POA reporting accuracy before using POA in hospital assessments tied to payments.
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Project description:There is a need for epidemiological and medical researchers to identify new biomarkers (biological markers) that are useful in determining exposure levels and/or for the purposes of disease detection. Often this process is stunted by high testing costs associated with evaluating new biomarkers. Traditionally, biomarker assessments are individually tested within a target population. Pooling has been proposed to help alleviate the testing costs, where pools are formed by combining several individual specimens. Methods for using pooled biomarker assessments to estimate discriminatory ability have been developed. However, all these procedures have failed to acknowledge confounding factors. In this paper, we propose a regression methodology based on pooled biomarker measurements that allow the assessment of the discriminatory ability of a biomarker of interest. In particular, we develop covariate-adjusted estimators of the receiver-operating characteristic curve, the area under the curve, and Youden's index. We establish the asymptotic properties of these estimators and develop inferential techniques that allow one to assess whether a biomarker is a good discriminator between cases and controls, while controlling for confounders. The finite sample performance of the proposed methodology is illustrated through simulation. We apply our methods to analyze myocardial infarction (MI) data, with the goal of determining whether the pro-inflammatory cytokine interleukin-6 is a good predictor of MI after controlling for the subjects' cholesterol levels.
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Project description:IntroductionPrevious studies have shown that patients admitted to the intensive care unit (ICU) after "office hours" are more likely to die. However these results have been challenged by numerous other studies. We therefore analysed this possible relationship between ICU admission time and in-hospital mortality in The Netherlands.MethodsThis article relates time of ICU admission to hospital mortality for all patients who were included in the Dutch national ICU registry (National Intensive Care Evaluation, NICE) from 2002 to 2008. We defined office hours as 08:00-22:00 hours during weekdays and 09:00-18:00 hours during weekend days. The weekend was defined as from Saturday 00:00 hours until Sunday 24:00 hours. We corrected hospital mortality for illness severity at admission using Acute Physiology and Chronic Health Evaluation II (APACHE II) score, reason for admission, admission type, age and gender.ResultsA total of 149,894 patients were included in this analysis. The relative risk (RR) for mortality outside office hours was 1.059 (1.031-1.088). Mortality varied with time but was consistently higher than expected during "off hours" and lower during office hours. There was no significant difference in mortality between different weekdays of Monday to Thursday, but mortality increased slightly on Friday (RR 1.046; 1.001-1.092). During the weekend the RR was 1.103 (1.071-1.136) in comparison with the rest of the week.ConclusionsHospital mortality in The Netherlands appears to be increased outside office hours and during the weekends, even when corrected for illness severity at admission. However, incomplete adjustment for certain confounders might still play an important role. Further research is needed to fully explain this difference.
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Project description:BackgroundGiven changes in the care and outcomes of acute myocardial infarction (AMI) patients over the past several decades, we sought to develop prediction models that could be used to generate accurate risk-adjusted mortality and readmission outcomes for hospitals in current practice across Canada.MethodsA Canadian national expert panel was convened to define appropriate AMI patients for reporting and develop prediction models. Preliminary candidate variable evaluation was conducted using Ontario patients hospitalized with a most responsible diagnosis of AMI from April 1, 2015 to March 31, 2018. National data from the Canadian Institute for Health Information was used to develop AMI prediction models. The main outcomes were 30-day all-cause in-hospital mortality and 30-day urgent all-cause readmission. Discrimination of these models (measured by c-statistics) was compared with that of existing Canadian Institute for Health Information models in the same study cohort.ResultsThe AMI mortality model was assessed in 54,240 Ontario AMI patients and 153,523 AMI patients across Canada. We observed a 30-day in-hospital mortality rate of 6.3%, and a 30-day all-cause urgent readmission rate of 10.7% in Canada. The final Canadian AMI mortality model included 12 variables and had a c-statistic of 0.834. For readmission, the model had 13 variables and a c-statistic of 0.679. Discrimination of the new AMI models had higher c-statistics compared with existing models (c-statistic 0.814 for mortality; 0.673 for readmission).ConclusionsIn this national collaboration, we developed mortality and readmission models that are suitable for profiling performance of hospitals treating AMI patients in Canada.
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Project description:Risk-adjustment schemes are used to monitor hospital performance, on the assumption that excess mortality not explained by case mix is largely attributable to suboptimal care. We have developed a model to estimate the proportion of the variation in standardised mortality ratios (SMRs) that can be accounted for by variation in preventable mortality. The model was populated with values from the literature to estimate a predictive value of the SMR in this context-specifically the proportion of those hospitals with SMRs among the highest 2.5% that fall among the worst 2.5% for preventable mortality. The extent to which SMRs reflect preventable mortality rates is highly sensitive to the proportion of deaths that are preventable. If 6% of hospital deaths are preventable (as suggested by the literature), the predictive value of the SMR can be no greater than 9%. This value could rise to 30%, if 15% of deaths are preventable. The model offers a 'reality check' for case mix adjustment schemes designed to isolate the preventable component of any outcome rate.
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Project description:ImportanceDelirium at admission is associated with increased hospital morbidity and mortality, but it may be missed in up to 70% of cases. Use of a predictive algorithm in an electronic medical record (EMR) system could provide critical information to target assessment of those with delirium at admission.ObjectivesTo develop and assess a prediction rule for delirium using 2 populations of veterans and compare this rule with previously confirmed rules.Design, setting, and participantsIn a diagnostic study, randomly selected EMRs of hospitalized veterans from the Veterans Affairs (VA) External Peer Review Program at 118 VA medical centers with inpatient facilities were reviewed for delirium risk factors associated with the National Institute for Health and Clinical Excellence (NICE) delirium rule in a derivation cohort (October 1, 2012, to September 30, 2013) and a confirmation cohort (October 1, 2013, to March 31, 2014). Delirium within 24 hours of admission was identified using key word terms. A total of 39 377 veterans 65 years or older who were admitted to a VA medical center for congestive heart failure, acute coronary syndrome, community-acquired pneumonia, and chronic obstructive pulmonary disease were included in the study.ExposureThe EMR calculated delirium risk.Main outcomes and measuresDelirium at admission as identified by trained nurse reviewers was the main outcome measure. Random forest methods were used to identify accurate risk factors for prevalent delirium. A prediction rule for prevalent delirium was developed, and its diagnostic accuracy was tested in the confirmation cohort. This consolidated NICE rule was compared with previously confirmed scoring algorithms (electronic NICE and Pendlebury NICE).ResultsA total of 27?625 patients were included in the derivation cohort (28 118 [92.2%] male; mean [SD] age, 75.95 [8.61] years) and 11 752 in the confirmation cohort (11 536 [98.2%] male; mean [SD] age, 75.43 [8.55] years). Delirium at admission was identified in 2343 patients (8.5%) in the derivation cohort and 882 patients (7.0%) in the confirmation cohort. Modeling techniques identified cognitive impairment, infection, sodium level, and age of 80 years or older as the dominant risk factors. The consolidated NICE rule (area under the receiver operating characteristic [AUROC] curve, 0.91; 95% CI, 0.91-0.92; P?<?.001) had significantly higher discriminatory function than the eNICE rule (AUROC curve, 0.81; 95% CI, 0.80-0.82; P?<?.001) or Pendlebury NICE rule (AUROC curve, 0.87; 95% CI, 0.86-0.88; P?<?.001). These findings were confirmed in the confirmation cohort.Conclusions and relevanceThis analysis identified preexisting cognitive impairment, infection, sodium level, and age of 80 years or older as delirium screening targets. Use of this algorithm in an EMR system could direct clinical assessment efforts to patients with delirium at admission.
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Project description:Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR ("Do Not Resuscitate") diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians.
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Project description:BackgroundRecent data have suggested the presence of a reciprocal relationship between COVID-19 and kidney function. To date, most studies have focused on the effect of COVID-19 on kidney function, whereas data regarding kidney function on the COVID-19 prognosis is scarce. Therefore, in this study, we aimed to investigate the association between eGFR on admission and the mortality rate of COVID-19.MethodsWe recruited 336 adult consecutive patients (male: 57.1%, mean age: 55.0±16.0 years) that were hospitalized with the diagnosis of COVID-19 in a tertiary care university hospital. Data were collected from the electronic health records of the hospital. On admission, eGFR was calculated using the CKD-EPI formula. Acute kidney injury was defined according to the KDIGO criteria. Binary logistic regression and Cox regression analyses were used to assess the relationship between eGFR on admission and in-hospital mortality of COVID-19.ResultsBaseline eGFR was under 60 mL/min/1.73m2 in 61 patients (18.2%). Acute kidney injury occurred in 29.2% of the patients. In-hospital mortality rate was calculated as 12.8%. Age-adjusted and multivariate logistic regression analysis (p: 0.005, odds ratio: 0.974, CI: 0.956-0.992) showed that baseline eGFR was independently associated with mortality. Additionally, age-adjusted Cox regression analysis revealed a higher mortality rate in patients with an eGFR under 60 mL/min/1.73m2.ConclusionsOn admission eGFR seems to be a prognostic marker for mortality in patients with COVID-19. We recommend that eGFR be measured in all patients on admission and used as an additional tool for risk stratification. Close follow-up should be warranted in patients with a reduced eGFR.
| S-EPMC7470363 | biostudies-literature
Project description:Background. There is a great deal of interest in evaluating hospital performance in order to monitor and improve health care quality. Increasingly, risk-adjusted performance measures are available to the public and statistical approaches for estimating these measures are considered. Some methods in use currently are based on 3-year aggregates of data since a small number of cases may lead to imprecise estimates and make it hard for stakeholders to detect differences across hospitals over time. However, if quality changes over time, a measure based on these data is a biased estimate of present performance. Methods. We present an alternative approach (weighted estimating equations [WEE]) for combining historical data in estimation that regulates the tradeoff between bias and precision in the measure of present performance. The WEE approach uses all available historical data through estimating functions that down-weight past data. Results. We compare the WEE approach to two current practices using a realistic dataset of the mortality of patients following an elective percutaneous coronary intervention procedure in New York State who meet certain criteria. The width of the uncertainty interval in the realistic example is up to 65% smaller and the difference is more pronounced for hospitals with a small number of cases. Conclusions. The advantage of this approach extends from the example dataset to other datasets. The WEE approach uses all available data rather than data from an arbitrary 3-year window. The effect of borrowing strength from historical data is a more precise estimate of present performance than current practices. Its advantages are important for the comparison of other aspects of medical performance, including surgical or medical practitioner performance.
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Project description:ImportancePresent-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting.ObjectiveTo assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS).Design, setting, and participantsThis comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020.Main outcomes and measuresChanges in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment.ResultsData from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure).Conclusions and relevanceThe findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.
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