Trends in hospital standardized mortality ratios for stroke in Japan between 2012 and 2016: a retrospective observational study

Amin, Rebeka; Kitazawa, Takefumi; Hatakeyama, Yosuke; Matsumoto, Kunichika; Fujita, Shigeru; Seto, Kanako; Hasegawa, Tomonori

doi:10.1093/intqhc/mzz091

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…An important caveat to note is that all patients that carry the diagnosis of acute ischemic stroke carry an increased risk of mortality compared to those without stroke on admission. 24 The mortality risk therefore may be intrinsically predicated with the stroke diagnosis as compared to the SARS-CoV-2 infection itself.…”

Section: A C C E P T E Dmentioning

confidence: 99%

Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19

Eskandar

Altschul²,

Ramos³

et al. 2021

Neurology

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Objective:The SARS-Cov2 virus is protean in its manifestations, affecting nearly every organ system. However, nervous system involvement and its impact on disease outcome are poorly characterized. The objective of the study is to determine if neurological syndromes are associated with increased risk of inpatient mortality.Methods:581 hospitalized patients with confirmed SARS-Cov2 infection, neurological involvement and brain-imaging were compared to hospitalized non-neurological COVID-19 patients. Four patterns of neurological manifestations were identified –acute stroke, new or recrudescent seizures, altered mentation with normal imaging, and neuro-COVID-19 complex. Factors present on admission were analyzed as potential predictors of in-hospital mortality, including sociodemographic variables, pre-existing comorbidities, vital-signs, laboratory values, and pattern of neurological manifestations. Significant predictors were incorporated into a disease-severity score. Patients with neurological manifestations were matched with patients of the same age and disease severity to assess the risk of death.Results:4711 patients with confirmed SARS-Cov2 infection were admitted to one medical system in New York City during a 6-week period. Of these, 581 (12%) had neurological issues of sufficient concern to warrant neuro-imaging. These patients were compared to 1743 non-neurological COVID-19 patients matched for age and disease-severity admitted during the same period. Patients with altered mentation (n=258, p =0.04, OR 1.39, CI 1.04 – 1.86) or radiologically confirmed stroke (n=55, p = 0.001, OR 3.1, CI 1.65-5.92) had a higher risk of mortality than age and severity-matched controls.Conclusions:The incidence of altered mentation or stroke on admission predicts a modest but significantly higher risk of in-hospital mortality independent of disease severity. While other biomarker factors also predict mortality, measures to identify and treat such patients may be important in reducing overall mortality of COVID-19.

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Section: A C C E P T E Dmentioning

confidence: 99%

Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19

Eskandar

Altschul²,

Ramos³

et al. 2021

Neurology

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“…Participation in the Medi-Target project was optional, and there were 182 participating hospitals in 2010, submitting about 500,000 claims data a year [8]. Details of the DPC database have been described elsewhere [12].…”

Section: Datamentioning

confidence: 99%

“…These data have been used in the design of health policies, disease management, and the analysis of healthcare processes and patient outcomes since its inception [9]. In Japan, several HSMR studies have been conducted [10][11][12][13] and the single-year HSMR model has already been created using administrative data for pneumonia [14]. Ian et al reported that diagnosis-specific HSMR is potentially a more fruitful method for monitoring mortality over time, which allows for the earlier identification of care deficiencies [4].…”

Section: Introductionmentioning

confidence: 99%

Capturing the trends in hospital standardized mortality ratios for pneumonia: a retrospective observational study in Japan (2010 to 2018)

Amin

Hatakeyama

Kitazawa

et al. 2020

Environ Health Prev Med

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Background: Pneumonia has a high human toll and a substantial economic burden in developed countries like Japan, where the crude mortality rate was 77.7 per 100,000 people in 2017. As this trend is going to continue with increasing number of the elderly multi-morbid population in Japan; monitoring performance over time is a social need to alleviate the disease burden. The study objective was to determine the characteristics of hospital standardized mortality ratios (HSMRs) for pneumonia in Japan from 2010 to 2018 to describe this trend. Methods: Data of the DPC (Diagnostic Procedures Combination) database were used, which is an administrative claims and discharge summary database for acute care in-patients in Japan. HSMRs were calculated using the actual and expected numbers of in-hospital deaths, the latter of which was calculated using logistic regression model, with a number of explanatory variables, e.g., age, sex, urgency of admission, mode of transportation, patient volume per month in each hospital, A-DROP score, and Charlson comorbidity index (CCI). We constructed two HSMR models: a single-year model, which included hospitals with > 10 in-patients per month and, a 9-year model, which included those hospitals with complete 9-year data. Predictive accuracy of the logistic models was assessed using c-index (area under receiver operating curve). Results: Total 230,372 patients were included for the analysis over the 9-year study period. Calculated HSMRs showed wide variation among hospitals. The proportion of hospitals with HSMR less than 100 increased from 36.4% in 2010 to 60.6% in 2018. Both models showed good predictive ability with a c-statistic of 0.762 for the 9-year model, and no less than 0.717 for the single-year model. Conclusion: This study denoted that HSMRs of pneumonia can be calculated using DPC data in Japan and revealed significant variations among hospitals with comparable case-mixes. Therefore, HSMR can be used as yet another measure to help improve quality of care over time if other indicators are examined in parallel and to get a clear picture of where hospitals excel and lack.

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“…A frequently used measure of risk-adjusted mortality is the standardized mortality ratio (SMR) [ 6 – 12 ]. Using indirect standardization, the SMR relates the observed mortality rate of a hospital to its expected mortality rate.…”

Section: Introductionmentioning

confidence: 99%

Can we trust the standardized mortality ratio? A formal analysis and evaluation based on axiomatic requirements

Roessler¹,

Schmitt²,

Schoffer³

2021

PLoS ONE

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Background The standardized mortality ratio (SMR) is often used to assess and compare hospital performance. While it has been recognized that hospitals may differ in their SMRs due to differences in patient composition, there is a lack of rigorous analysis of this and other—largely unrecognized—properties of the SMR. Methods This paper proposes five axiomatic requirements for adequate standardized mortality measures: strict monotonicity (monotone relation to actual mortality rates), case-mix insensitivity (independence of patient composition), scale insensitivity (independence of hospital size), equivalence principle (equal rating of hospitals with equal actual mortality rates in all patient groups), and dominance principle (better rating of unambiguously better performing hospitals). Given these axiomatic requirements, effects of variations in patient composition, hospital size, and actual and expected mortality rates on the SMR were examined using basic algebra and calculus. In this regard, we distinguished between standardization using expected mortality rates derived from a different dataset (external standardization) and standardization based on a dataset including the considered hospitals (internal standardization). The results were illustrated by hypothetical examples. Results Under external standardization, the SMR fulfills the axiomatic requirements of strict monotonicity and scale insensitivity but violates the requirement of case-mix insensitivity, the equivalence principle, and the dominance principle. All axiomatic requirements not fulfilled under external standardization are also not fulfilled under internal standardization. In addition, the SMR under internal standardization is scale sensitive and violates the axiomatic requirement of strict monotonicity. Conclusions The SMR fulfills only two (none) out of the five proposed axiomatic requirements under external (internal) standardization. Generally, the SMRs of hospitals are differently affected by variations in case mix and actual and expected mortality rates unless the hospitals are identical in these characteristics. These properties hamper valid assessment and comparison of hospital performance based on the SMR.

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Trends in hospital standardized mortality ratios for stroke in Japan between 2012 and 2016: a retrospective observational study

Cited by 8 publications

References 21 publications

Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19

Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19

Capturing the trends in hospital standardized mortality ratios for pneumonia: a retrospective observational study in Japan (2010 to 2018)

Can we trust the standardized mortality ratio? A formal analysis and evaluation based on axiomatic requirements

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