Trends in COVID-19 Risk-Adjusted Mortality Rates

2020 ◽  
Author(s):  
Leora I Horwitz ◽  
Simon A Jones ◽  
Robert J Cerfolio ◽  
Fritz Francois ◽  
Joseph Greco ◽  
...  
Keyword(s):  
New York ◽  
Hospital Mortality ◽  
Health System ◽  
Vital Signs ◽  
Patient Characteristics ◽  
Mortality Rates ◽  
Standardized Mortality Ratio ◽  
Marginal Effect ◽  
Average Probability ◽  
Mortality Ratio

Early reports showed high mortality from coronavirus disease 2019 (COVID-19). Mortality rates have recently been lower, raising hope that treatments have improved. However, patients are also now younger, with fewer comorbidities. We explored whether hospital mortality was associated with changing demographics at a 3-hospital academic health system in New York. We examined in-hospital mortality or discharge to hospice from March through August 2020, adjusted for demographic and clinical factors, including comorbidities, admission vital signs, and laboratory results. Among 5,121 hospitalizations, adjusted mortality dropped from 25.6% (95% CI, 23.2-28.1) in March to 7.6% (95% CI, 2.5-17.8) in August. The standardized mortality ratio dropped from 1.26 (95% CI, 1.15-1.39) in March to 0.38 (95% CI, 0.12-0.88) in August, at which time the average probability of death (average marginal effect) was 18.2 percentage points lower than in March. Data from one health system suggest that mortality from COVID-19 is decreasing even after accounting for patient characteristics.

scholarly journals Hospital Standardized Mortality Ratio: Consequences of Adjusting Hospital Mortality with Indirect Standardization

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e59160 ◽  
Author(s):  
Maurice E. Pouw ◽  
Linda M. Peelen ◽  
Hester F. Lingsma ◽  
Daniel Pieter ◽  
Ewout Steyerberg ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Standardized Mortality Ratio ◽  
Mortality Ratio ◽  
Indirect Standardization

scholarly journals Relationship between Volume and Survival in Closed Intensive Care Units Is Weak and Apparent Only in Mechanically Ventilated Patients

2013 ◽  
Vol 119 (4) ◽  
pp. 871-879 ◽  
Author(s):  
Rafael Fernández ◽  
Susana Altaba ◽  
Lluis Cabre ◽  
Victoria Lacueva ◽  
Antonio Santos ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Standardized Mortality Ratio ◽  
Hospital Level ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
Low Volume ◽  
Predicted Risk ◽  
Ventilated Patients

Abstract Background: Recent studies have found an association between increased volume and increased intensive care unit (ICU) survival; however, this association might not hold true in ICUs with permanent intensivist coverage. Our objective was to determine whether ICU volume correlates with survival in the Spanish healthcare system. Methods: Post hoc analysis of a prospective study of all patients admitted to 29 ICUs during 3 months. At ICU discharge, the authors recorded demographic variables, severity score, and specific ICU treatments. Follow-up variables included ICU readmission and hospital mortality. Statistics include logistic multivariate analyses for hospital mortality according to quartiles of volume of patients. Results: The authors studied 4,001 patients with a mean predicted risk of death of 23% (range at hospital level: 14–46%). Observed hospital mortality was 19% (range at hospital level: 11–35%), resulting in a standardized mortality ratio of 0.81 (range: 0.5–1.3). Among the 1,923 patients needing mechanical ventilation, the predicted risk of death was 32% (14–60%) and observed hospital mortality was 30% (12–61%), resulting in a standardized mortality ratio of 0.96 (0.5–1.7). The authors found no correlation between standardized mortality ratio and ICU volume in the entire population or in mechanically ventilated patients. Only mechanically ventilated patients in very low-volume ICUs had slightly worse outcome. Conclusion: In the currently studied healthcare system characterized by 24/7 intensivist coverage, the authors found wide variability in outcome among ICUs even after adjusting for severity of illness but no relationship between ICU volume and outcome. Only mechanically ventilated patients in very low-volume centers had slightly worse outcomes.

scholarly journals Canadian in-hospital mortality for patients with emergency-sensitive conditions: a retrospective cohort study

2019 ◽  
Author(s):  
Simon Berthelot ◽  
Eddy S. Lang ◽  
Hude Quan ◽  
Henry T. Stelfox
Keyword(s):  
Hospital Mortality ◽  
Prince Edward Island ◽  
Standardized Mortality Ratio ◽  
Discharge Data ◽  
Important Variation ◽  
Mortality Ratio ◽  
Hospital Deaths ◽  
Canadian Provinces ◽  
Improve Health ◽  
Sensitive Conditions

Abstract BACKGROUND: The emergency department sensitive hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve health outcomes. This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. METHODS: Hospital discharge data were analyzed from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010-11 or 2011-12) to the expected deaths for the same patients during the reference year (2009-10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year. Aggregated provincial ED-HSMR values were calculated. A HSMR value above or below 100 respectively means that more or fewer deaths than expected occurred within a province. RESULTS: During the study period, 1,335,379 patients were admitted to hospital in Canada with an emergency-sensitive condition as the most responsible diagnosis. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010-11 and 2011-12 in Newfoundland [124.3 (116.3-132.6); & 117.6 (110.1-125.5)] and Nova Scotia [116.4 (110.7-122.5) & 108.7 (103.0-114.5)], while mortality was as expected in Prince Edward Island [99.9 (86.5-114.8) & 100.7 (87.5-115.3)] and Manitoba [99.2 (94.5-104.1) & 98.3 (93.5-103.3)], and less than expected in all other provinces and territories. CONCLUSIONS: Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The results should trigger in-depth evaluations to identify the causes for regional variations.

scholarly journals Canadian in-hospital mortality for patients with emergency-sensitive conditions: a retrospective cohort study

2019 ◽  
Author(s):  
Simon Berthelot ◽  
Eddy S. Lang ◽  
Hude Quan ◽  
Henry T. Stelfox
Keyword(s):  
Hospital Mortality ◽  
Prince Edward Island ◽  
Standardized Mortality Ratio ◽  
Discharge Data ◽  
Process Indicators ◽  
Important Variation ◽  
Mortality Ratio ◽  
Hospital Deaths ◽  
Canadian Provinces ◽  
Sensitive Conditions

Abstract BACKGROUND: The emergency department (ED) sensitive hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve health outcomes. This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. METHODS: Hospital discharge data were analyzed from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010-11 or 2011-12) to the expected deaths for the same patients during the reference year (2009-10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year. Aggregated provincial ED-HSMR values were calculated. A HSMR value above or below 100 respectively means that more or fewer deaths than expected occurred within a province. RESULTS: During the study period, 1,335,379 patients were admitted to hospital in Canada with an emergency-sensitive condition as the most responsible diagnosis. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010-11 and 2011-12 in Newfoundland [124.3 (116.3-132.6); & 117.6 (110.1-125.5)] and Nova Scotia [116.4 (110.7-122.5) & 108.7 (103.0-114.5)], while mortality was as expected in Prince Edward Island [99.9 (86.5-114.8) & 100.7 (87.5-115.3)] and Manitoba [99.2 (94.5-104.1) & 98.3 (93.5-103.3)], and less than expected in all other provinces and territories. CONCLUSIONS: Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The ED-HSMR may be a useful outcome indicator to complement existing process indicators in measuring ED performance.

USE OF CODED MORTALITY DATA TO ASSESS AREA CANCER RATES: IMPACT OF RESIDENCE REPORTING AND CODING ERRORS

1990 ◽  
Vol 132 (supp1) ◽  
pp. 178-182 ◽  
Author(s):  
ALLAN N. WILLIAMS ◽  
REBECCA A. JOHNSON ◽  
ALAN P. BENDER
Keyword(s):  
Rural Areas ◽  
Cancer Mortality ◽  
Mortality Rates ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Death Certificates ◽  
Total Cancer ◽  
Mortality Ratio ◽  
Cancer Rates ◽  
Standardized Mortality Ratios

Abstract In spite of their limitations, mortality data are used in many epidemiologic and public health settings. In this investigation, the authors examined the extent to which community cancer mortality rates were affected by incorrect reporting or coding of residence on death certificates. Observed and expected cancer mortality for two adjacent communities in northern rural Minnesota for the periods 1970–1974 and 1980–1984 were obtained from computerized state mortality data. Using statewide rates to obtain expected values, standardized mortality ratios for total cancers for both periods combined were 138 for men (101 observed deaths) and 148 for women (86 observed deaths). These excesses were statistically significant (p < 0.05). However, after review of data from the actual death certificates, city maps, and information from city officials, 44 of the 187 total cancer deaths (24%) were found to have had an incorrectly reported or coded residence status. After removal of these cases, the standardized mortality ratio for total cancers for males went from 138 to 107, and for females the standardized mortality ratio went from 148 to 111. No standardized mortality ratios remained statistically significant These findings may have implications for those who use mortality data for assessing cancer rates in communities in rural areas.

scholarly journals LO085: Canadian in-hospital mortality for patients with emergency-sensitive conditions

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S59-S59
Author(s):  
S. Berthelot ◽  
E. Lang ◽  
H. Quan ◽  
H. Stelfox
Keyword(s):  
Hospital Mortality ◽  
Prince Edward Island ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Standardized Mortality Ratio ◽  
Important Variation ◽  
Mortality Ratio ◽  
Hospital Deaths ◽  
Canadian Provinces ◽  
Sensitive Conditions

Introduction: The emergency department (ED) hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve outcomes (emergency-sensitive conditions). This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. Methods: Data were extracted from hospital discharge databases from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010-11 or 2011-12) to the expected deaths for the same patients during the reference year (2009-10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year for different hospital peer-groups (teaching, large, medium and small hospitals) adjusted for comorbidities, age, diagnosis, and hospital length of stay. Thirty-seven validated emergency-sensitive conditions were included (e.g., stroke, sepsis, shock). Aggregated provincial ED-HSMR values were derived from patient-level probabilities of death. A HSMR above or below 100 respectively means that more or fewer deaths than expected occurred in hospital within a province. Results: During the study period, 1,335,379 patients were admitted to 629 hospitals across 11 provinces and territories with an emergency-sensitive condition as the most responsible diagnosis, of which 8.9% died. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010-11 and 2011-12 in Newfoundland [124.3 (116.3-132.6) & 117.6 (110.1-125.5)] and Nova Scotia [116.4 (110.7-122.5) & 108.7 (103.0-114.5)], while mortality was as expected in Prince Edward Island and Manitoba, and less than expected in other provinces and territories [Territories 67.3 (48.3-91.3) & 73.2 (55.0-95.5); New Brunswick 87.7 (82.5-93.1) & 90.4 (85.2-95.8); British Columbia 92.0 (89.6-94.4) & 87.1 (84.9-89.3); Saskatchewan 92.3 (87.1-97.4) & 90.8 (86.2-95.6); Ontario 94.0 (92.6-95.4) & 88.0 (86.6-89.3); Alberta 94.1 (91.1-97.2) & 91.0 (88.2-93.9); Québec 95.7 (93.8-97.6) & N/A]. Conclusion: Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The results should trigger more in-depth evaluations to identify the causes for these regional variations.

scholarly journals Post-anticoagulant D-dimer is a highly prognostic biomarker of COVID-19 mortality

2021 ◽  
pp. 00018-2021
Author(s):  
Xiaoyu Song ◽  
Jiayi Ji ◽  
Boris Reva ◽  
Himanshu Joshi ◽  
Anna Pamela Calinawan ◽  
...  
Keyword(s):  
New York ◽  
Hospital Mortality ◽  
Research Question ◽  
Vital Signs ◽  
Adult Patients ◽  
Logistic Regression Models ◽  
Clinical Biomarkers ◽  
Therapeutic Anticoagulation ◽  
Emerging Treatments ◽  
D Dimer

Research QuestionClinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe COVID-19 illness. In this study, we determine whether changes in D-dimer levels after anticoagulation are independently predictive of in-hospital mortality.Study DesignAdult patients hospitalised for severe COVID-19 who received therapeutic anticoagulation for thromboprophylaxis were identified from a large COVID-19 database of the Mount Sinai Health System in New York City. We studied the ability of post-anticoagulant D-dimer levels to predict in-hospital mortality, while taking into consideration 65 other clinically important covariates including patient demographics, comorbidities, vital signs and several laboratory tests.Results1835 adult patients with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalisation were included. Overall, 26% of patients died in the hospital. Significantly different in-hospital mortality rates were observed in patient groups based on mean D-dimer levels and trend following anticoagulation: 49% for the high mean-increase trend (HI) group; 27% for the high-decrease (HD) group; 21% for the low-increase (LI) group; and 9% for the low-decrease (LD) group (p<0.001). Using penalised logistic regression models to simultaneously analyze 67 clinical variables, the HI (adjusted odds ratios [ORadj]: 6.58, 95% CI 3.81–11.16), LI (ORadj: 4.06, 95% CI 2.23–7.38) and HD (ORadj: 2.37; 95% CI 1.37–4.09) D-dimer groups (reference: LD group) had the highest odds for in-hospital mortality among all clinical features.ConclusionChanges in D-dimer levels and trend following anticoagulation are highly predictive of in-hospital mortality and may help guide resource allocation and future studies of emerging treatments for severe COVID-19.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257003
Author(s):  
Martin Roessler ◽  
Jochen Schmitt ◽  
Olaf Schoffer
Keyword(s):  
Mortality Rates ◽  
Hospital Performance ◽  
Case Mix ◽  
Standardized Mortality Ratio ◽  
Strict Monotonicity ◽  
Hospital Size ◽  
Mortality Ratio ◽  
Dominance Principle ◽  
Internal Standardization ◽  
Expected Mortality

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.

How Well Do Hospital Mortality Rates Reported in the New York State CABG Report Card Predict Subsequent Hospital Performance?

Medical Care ◽  
2010 ◽  
Vol 48 (5) ◽  
pp. 466-471 ◽  
Author(s):  
Laurent G. Glance ◽  
Andrew W. Dick ◽  
Dana B. Mukamel ◽  
Yue Li ◽  
Turner M. Osler
Keyword(s):  
New York ◽  
Hospital Mortality ◽  
New York State ◽  
Mortality Rates ◽  
Hospital Performance ◽  
Report Card ◽  
York State
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