A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients

Min, Hua; Avramovic, Sanja; Wojtusiak, Janusz; Khosla, Rahul; Fletcher, Ross D.; Alemi, Farrokh; Kheirbek, Raya

doi:10.1089/jpm.2015.0392

Cited by 23 publications

(46 citation statements)

References 60 publications

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“…Characteristics of the mortality prediction models and underlying derivation cohorts are presented in Table 1. In all, 19 mortality prediction models (44%) were developed using prospectively collected data specifically gathered for the development of the prediction model, 6,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] whereas 24 (56%) were developed using either retrospective data [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] or prospective data previously collected for other purposes. [45][46][47][48][49] The start of data collection for the development cohorts spanned 36 years (1979-2015), and the duration of the cohort studies varying from 2 months up to 10 years for each cohort.…”

Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

“…Two mortality prediction models (4.7%) did not report the timespan during which their development cohort was assembled. 22,33 In all, 31 mortality prediction models (74%) were developed in a single country, 14,[18][19][20][21][22][23][24][25][26][27]29,31,[33][34][35][36][37][38][39][40][41][42][43][44][45]47,49 six (14%) in neighbouring countries (two or more) 6,13,28,30,32,46 and five (12%) were developed in multiple countries worldwide. [15][16][17]48 The number of patients included in the development databases ranged from 232 to 731 611 patients with a median of 4,895 (IQR 528-35 878).…”

Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

“…35 In all, 11 mortality prediction models (26%) did not specify age. 6,13,23,25,29,31,36,38,42,46 The number of variables included in the mortality prediction models varied from 5 up to 5695, with a median of 16 (IQR 9-24).…”

Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Mortality prediction models in the adult critically ill: A scoping review

Keuning

Kaufmann

Wiersema

et al. 2019

Acta Anaesthesiol Scand

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Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Mortality prediction models in the adult critically ill: A scoping review

Keuning

Kaufmann

Wiersema

et al. 2019

Acta Anaesthesiol Scand

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“…In a study of patients in intensive care, we compared the accuracy of the MM Index to 13 physiological markers 58. These markers included the following: (1) sodium, (2) blood urea nitrogen, (3) creatinine, (4) glucose, (5) albumin, (6) bilirubin, (7) white blood cell count, (8) hematocrit, (9) PaO2, (10) PaCO2, (11) pH, (12) eGFR, and (13) lactic acid.…”

Section: Accuracy Of the MM Index Compared To Physiological Markersmentioning

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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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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“…Accurate prediction of prognosis of patients admitted to Intensive Care Units (ICUs) is very important for the patients and their family, assessing the quality of care, clinical management of patients, and predicting the likelihood of readmission ( 1 ). Early identification of high-risk patients, permit resources to be used more suitably and prevents avoidable morbidities and deaths ( 2 , 3 ).…”

Section: Introductionmentioning

confidence: 99%

Predictive factors associated with mortality and discharge in intensive care units: a retrospective cohort study

Ghorbani¹,

Ghaem²,

Rezaianzadeh³

et al. 2018

Electron Physician

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Background and aimAccurate prediction of prognosis of patients admitted to intensive care units (ICUs) is very important for the clinical management of the patients. The present study aims to identify independent factors affecting death and discharge in ICUs using competing risk modeling.MethodsThis retrospective cohort study was conducted on enrolling 880 patients admitted to emergency ICU in Namazi hospital, Shiraz University of Medical Sciences, Shiraz, Iran during 2013–2015. The data was collected from patients’ medical records using a researcher-made checklist by a trained nurse. Competing risk regression models were fitted for the factors affecting the occurrence of death and discharge in ICU. Data analysis was conducted using STATA 13 and R 3.3.3 software.ResultsAmong these patients, 682 (77.5%) were discharged and 157 (17.8%) died in the ICU. The patients’ mean ± SD age was 48.90±19.52 yr. Among the study patients, 45.57% were female and 54.43% were male. In the competing risk model, age (Sub-distribution Hazard Ratio (SHR)) =1.02, 95% CI: 1.007–1.032), maximum heart rate (SHR=1.009, 95% CI: 1.001–1.019), minimum sodium level (SHR=1.035, 95% CI: 1.007–1.064), PH (SHR=7.982, 95% CI: 1.259–50.61), and bilirubin (SHR=1.046, 95% CI: 1.015–1.078) increased the risk of death, while maximum sodium level (SHR=0.946, 95% CI: 0.908–0.986) and maximum HCT (SHR=0.938, 95% CI: 0.882–0.998) reduced the risk of death.ConclusionIn conclusion, the results of this study revealed several variables that were effective in ICU length of stay (LOS). The variables that independently influenced time-to-discharge were age, maximum systolic blood pressure, minimum HCT, maximum WBC, and urine output, maximum HCT and Glasgow coma score. The results also showed that age, maximum heart rate, maximum sodium level, PH, urine output, and bilirubin, minimum sodium level and maximum HCT were the predictors of death. Furthermore, our findings indicated that the competing risk model was more appropriate than the Cox model in evaluating the predictive factors associated with the occurrence of death and discharge in patients hospitalized in ICUs. Hence, this model could play an important role in managers’ and clinicians’ decision-making and improvement of the standard of care in ICUs.

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A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients

Abstract: The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.

Cited by 23 publications

References 60 publications

Mortality prediction models in the adult critically ill: A scoping review

Mortality prediction models in the adult critically ill: A scoping review

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

Predictive factors associated with mortality and discharge in intensive care units: a retrospective cohort study

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