A Mathematical Theory for Identifying and Measuring Severity of Episodes of Care

Background:The Multimorbidity (MM) Index predicts the prognosis of patients from their diagnostic history. In contrast to existing approaches with broad diagnostic categories, it treats each diagnosis as a separate independent variable using individual International Classification of Disease, Revision 9 (ICD-9) codes.Objective:This paper describes the MM Index, reviews the published data on its accuracy, and provides procedures for implementing the Index within electronic health record (EHR) systems. Methods: The MM Index was tested on various patient populations by using data from the United States Department of Veterans Affairs data warehouse and claims data within the Healthcare Cost and Utilization Project of the Agency for Health Care Research and Quality.Results:In cross-validated studies, the MM Index outperformed prognostic indices based on physiological markers, such as CD4 cell counts in HIV/AIDS, HbAlc levels in diabetes, ejection fractions in heart failure, or the 13 physiological markers commonly used for patients in intensive care units. When predicting the prognosis of nursing home patients by using the cross-validated area under a receiver operating characteristic (ROC) curve, the MM Index was 15 percent outperformed the Quan variant of the Charlson Index, 27 percent more accurate than the Deyo variant of the Charlson Index, and 22 percent more accurate than the von Walraven variant of the Elixhauser Index. For patients in intensive care units, the MM Index was 13 percent outperformed the cross-validated area under ROC associated with Elixhauser’s categories. The MM Index also demonstrated greater accuracy than a number of commercially available measures of illness severity; including a fivefold greater accuracy than the All Patient Refined Diagnosis-Related Groups and a threefold greater accuracy than All Payer Severity-Adjusted Diagnosis-Related Groups.Conclusion:The MM Index is statistically more accurate than many existing measures of prognosis. The magnitude of improvement is large and may lead to a clinically meaningful difference in patient care. Given the large improvements in accuracy, the use of the MM Index for policy and comparative effectiveness analysis is recommended.

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“…In both of these situations, a likelihood ratio cannot be calculated. In these circumstances, Alemi40,41 proposed using the following formulas:

L_{Diagnosis} = {\begin{array}{l} left & if all survive & 1 / (n + 1) \\ left & if none survive & n + 1 \end{array}

where n indicates the number of patients with the diagnosis.…”

Section: Adjustments For Diseases With No Mortality or Mortality In Amentioning

confidence: 99%

“…Similar SQL code is also available for measurement of episodes of illness 71. Because the source of data may include millions of records, the code is written in steps, with each step generating a temporary or a permanent file that is used in subsequent steps.…”

mentioning

confidence: 99%

The Multi-Morbidity Index: A Tool for Assessing the Prognosis of Patients from History of Illness

Alemi

Levy

Kheirbek

2016

eGEMs

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“…The Hospice ELigibility Prediction (HELP) Index was calculated using the steps proposed by Alemi and Walter 29 and later validated in claims-based measures of severity for childhood diseases 30 and in predicting severity of heart failure. 31 The independent variables used to predict probability of mortality in the next 6-moanths included:…”

Section: Measurement Of Help Indexmentioning

confidence: 99%

Predictors of Six-Month Mortality among Nursing Home Residents: Diagnoses May Be More Predictive than Functional Disability

Levy

Kheirbek

Alemi

et al. 2015

Journal of Palliative Medicine

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“…This is similar in concept to the approach first examined by Alemi and Walter's Severity Index. 18 A brief description of the procedure is provided here:…”

Section: Prognosis Of Chf Comorbiditiesmentioning

confidence: 99%

Trajectory of Illness for Patients with Congestive Heart Failure

Kheirbek

Alemi

Citron

et al. 2013

Journal of Palliative Medicine

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Five typical illness trajectories have been described for patients with progressive heart failure. The needs of patients and their caregivers are likely to vary according to the trajectory patients are following. Contrary to reports in the literature about unexpected death in patients with CHF, the majority of decedents in our study had a predictable and gradual progression toward death. Recognizing these trajectories may help clinicians implement an appropriate plan to meet the needs of patients and their caregivers.

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A Mathematical Theory for Identifying and Measuring Severity of Episodes of Care

Abstract: These data suggest that our proposed mathematical approach is reasonable and produces severity scores that are predictive of objective criteria such as cost of care.

Cited by 10 publications

References 19 publications

The Multi-Morbidity Index: A Tool for Assessing the Prognosis of Patients from History of Illness

The Multi-Morbidity Index: A Tool for Assessing the Prognosis of Patients from History of Illness

Predictors of Six-Month Mortality among Nursing Home Residents: Diagnoses May Be More Predictive than Functional Disability

Trajectory of Illness for Patients with Congestive Heart Failure

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