Comparative influence of Acute Illness Severity and comorbidity on mortality

Reduced 30-day in-hospital but increased long-term mortality for weekend vs weekday acute medical admission

BACKGROUND

Acute medical admission at the weekend has been reported to be associated with increased mortality. We aimed to assess 30-day in-hospital mortality and subsequent follow-up of all community deaths following discharge for acute medical admission to our institution over 21 years.

METHODS

We employed a database of all acute medical admissions to our institution over 21 years (2002-2023). We compared 30-day in-hospital mortality by weekend (Saturday/Sunday) or weekday (Tuesday/Wednesday) admission. Outcome post-discharge was determined from the National Death Register to December 2021. Predictors of 30-day in-hospital and long-term mortality were analysed by logistic regression or Cox proportional hazards models.

RESULTS

The study population consisted of 109,232 admissions in 57,059 patients. A weekend admission was associated with a reduced 30-day in-hospital mortality, odds ratio (OR) 0.70 (95%CI 0.65, 0.76). Major predictors of 30-day in-hospital mortality were acute illness severity score (AISS) OR 6.9 (95%CI 5.5, 8.6) and comorbidity score OR 2.4 (95%CI 1.2, 4.6). At a median follow-up of 5.9 years post-discharge, 19.0% had died. The strongest long-term predictor of mortality was admission AISS OR 6.7 (95%CI 4.6, 9.9). The overall survival half-life after hospital discharge was 16.6 years. Survival was significantly worse for weekend admissions at 20.8 years compared to weekday admissions at 13.3 years.

CONCLUSION

Weekend admission of acute medical patients is associated with reduced 30-day in-hospital mortality but reduced long-term survival.

The Impact of Frailty, Oropharyngeal Dysphagia and Malnutrition on Mortality in Older Patients Hospitalized for Covid-19

COVID-19 hospital mortality is higher among older patients through as yet little-known factors. We aimed to assess the effect of frailty (FR), oropharyngeal dysphagia (OD) and malnutrition (MN) on mortality in hospitalized COVID-19 older patients. Prospective cohort study of older patients (>70 years) with COVID-19 admitted to a general hospital from April 2020 to January 2021. Patients were evaluated on admission, discharge and at 1- and 3-months follow up. FR was assessed with FRAIL-VIG, OD with Volume-Viscosity Swallowing Test and MN with GLIM criteria. Clinical characteristics and outcomes, including intra-hospital, 1- and 3-month mortality, were analyzed. 258 patients were included (82.5±7.6 years; 58.9% women); 66.7% had FR (mild 28.7%, moderate 27.1% and severe 10.9%); 65.4%, OD and 50.6%, MN. OD prevalence increased from non-FR patients through the severity levels of FR: mild, moderate and severe (29.8%, 71.6%, 90.0%, 96.2%; p<0.0001, respectively), but not that of MN (50.6%, 47.1%, 52.5%, 56.0%). Mortality over the whole study significantly increased across FR categories (9.3% non-FR; 23.0% mild; 35.7% moderate; 75.0% severe; p<.001). Functionality (Barthel pre-admission, HR=0.983, CI-95%:0.973-0.993; p=0.001), OD (HR=2.953, CI-95%:0.970-8.989; p=0.057) and MN (HR=4.279, CI-95%:1.658-11.049; p=0.003) were independent risk factors for intra-hospital mortality. FR, OD and MN are highly prevalent conditions in older patients hospitalized with COVID-19. Functionality, OD and MN were independent risk factors for intra-hospital mortality.

Short- and long-term mortality following acute medical admission

BACKGROUND

Short-term in-hospital mortality following acute medical admission has been widely investigated. Longer term mortality, particularly out-of-hospital mortality, has been less well studied.

AIM

The aim of this study is to evaluate short- and long-term mortality, and predictors of such, following acute medical admission.

DESIGN

Retrospective database study.

METHODS

We evaluated all acute medical admissions to our institution over 10 years (2002-11) with a minimum of a further 10 years follow-up to 2021 using the Irish National Death Register. Predictors of 30-day in-hospital and long-term mortality were analysed with logistic and Cox regression, with loss of life years estimated.

RESULTS

The 2002-11 cohort consisted of 62 184 admissions in 35 140 patients. 30-Day in hospital mortality (n = 3646) per patient was 10.4% and per admission was 5.9%. There were an additional 11 440 longer-term deaths by 2021-total mortality was 15 086 (42.9%). Deaths post hospital discharge had median age at admission of 75.4 years [interquartile range (IQR) 63.7, 82.8] and died at median age of 80 years (IQR 69, 87). The half-life of survival following admission was 195 months-representing a short fall of 8 life years (32.9%) compared with the projected population reference of 24.3 years. Age [odds ratio (OR) 1.73 (95% confidence interval (CI) 1.64, 1.81)], acute illness severity score [OR 1.39 (95% CI 1.36, 1.43)] and comorbidity score [OR 1.09 (95% CI 1.08, 1.10)] predicted long-term mortality.

CONCLUSION

Similar factors influence both short- and long-term mortality following acute medical admission, the magnitude of effect is attenuated over time.

Red Cell Distribution Width as a Prognostic Indicator in Acute Medical Admissions

The red cell distribution width (RDW) is the coefficient of variation of the mean corpuscular volume (MCV). We sought to evaluate RDW as a predictor of outcomes following acute medical admission. We studied 10 years of acute medical admissions (2002-2011) with subsequent follow-up to 2021. RDW was converted to a categorical variable, Q1 < 12.9 fl, Q2-Q4 ≥ 12.9 and <15.7 fL and Q5 ≥ 15.7 fL. The predictive value of RDW for 30-day in-hospital and long-term mortality was evaluated with logistic and Cox regression modelling. Adjusted odds ratios (aORs) were calculated and loss of life years estimated. There were 62,184 admissions in 35,140 patients. The 30-day in-hospital mortality (n = 3646) occurred in 5.9% of admissions. An additional 15,086 (42.9%) deaths occurred by December 2021. Admission RDW independently predicted 30-day in-hospital mortality aOR 1.93 (95%CI 1.79, 2.07). Admission RDW independently predicted long-term mortality aOR 1.04 (95%CI 1.02, 1.05). Median survival post-admission was 189 months. For those with admission RDW in Q5, observed survival half-life was 133 months-this represents a shortfall of 5.7 life years (33.9%). In conclusion, admission RDW independently predicts 30-day in-hospital and long-term mortality.

The Norton scale is an important predictor of in-hospital mortality in internal medicine patients

BACKGROUND

The Norton scale, a marker of patient frailty used to predict the risk of pressure ulcers, but the predictive value of the Norton scale for in-hospital mortality after adjustment for a wide range of demographic, and abnormal admission laboratory test results shown in themselves to have a high predictive value for in-hospital mortality is unclear.

AIM

The study aims to determine the value of the Norton scale and the presence of a urinary catheter in predicting in hospital mortality.

METHODS

The study population included all acutely admitted adult patients in 2020 through October 2021 to one of three internal medicine departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. The main objective was to (a) identify the variables associated with the Norton Scale and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.

RESULTS

The Norton scale was associated with an older age, female gender, presence of a urinary catheter, and abnormal laboratory tests. The odds of in-hospital mortality in those with intermediate, high, and very high Norton scale risk groups were 3.10 (2.23-3.56), 6.48 (4.02-10.46), and 12.27 (7.37-20.44), respectively, after adjustment for the remaining predictors. Adding the Norton scale and the presence of a urinary catheter to the prediction logistic regression model that included age, gender, and abnormal laboratory test results increased the c-statistic from 0.870 (0.864-0.876) to 0.908 (0.902-0.913).

CONCLUSIONS

The Norton scale and presence of a urinary catheter are important predictors of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

The presence of a urinary catheter is an important predictor of in-hospital mortality in internal medicine patients

RATIONALE AND OBJECTIVE

Mortality rates are used to assess the quality of hospital care after appropriate adjustment for case-mix. Urinary catheters are frequent in hospitalized adults and might be a marker of patient frailty and illness severity. However, we know of no attempts to estimate the predictive value of indwelling catheters for specific patient outcomes. The objective of the present study was to (a) identify the variables associated with the presence of a urinary catheter and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.

METHODS

The study population included all acutely admitted adult patients in 2020 (exploratory cohort) and January-October 2021 (validation cohort) to internal medicine, cardiology and intensive care departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. There were no exclusion criteria. The predictor variables were the presence of a urinary catheter on admission, age, gender, comorbidities and admission laboratory test results. We used bivariate and multivariate logistic regression to test the associations between the presence of a urinary catheter and mortality after adjustment for the remaining independent variables on admission.

RESULTS

The presence of a urinary catheter was associated with other independent variables. In 2020, the odds of in-hospital mortality in patients with a urinary catheter before and after adjustment for the remaining predictors were 14.3 (11.6-17.7) and 6.05 (4.78-7.65), respectively. Adding the presence of a urinary catheter to the prediction logistic regression model increased its c-statistic from 0.887 (0.880-0.894) to 0.907 (0.901-0.913). The results of the validation cohort reduplicated those of the exploratory cohort.

CONCLUSIONS

The presence of a urinary catheter on admission is an important and independent predictor of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

Prognostic value and clinical utility of NT-proBNP in acute emergency medical admissions

BACKGROUND

NT-proB-type natriuretic peptide (NT-proBNP) is a frequently utilized test in congestive cardiac failure. There is little data on its utility in unselected emergency medical admissions.

AIM

This study aims to investigate the clinical utility and prognostic value of NT-proBNP in emergency medical admissions and to determine whether such testing influenced downstream investigations and length of stay (LOS).

METHODS

We report on NT-proBNP tests performed in emergency medical admissions in a 2005/2006 and subsequent 7-year (2014-2020) retrospective cohort. We assessed 30-day in-hospital mortality with a multivariable logistic regression model. The utilization of procedures/services was related to LOS with zero-truncated Poisson regression.

RESULTS

There were 64,212 admissions in 36,252 patients. Patients with a NT-proBNP test were significantly older at 75.3 years vs. 63.0 years and had longer LOS -9.4 days vs. 4.9 days. They had higher acute illness severity and comorbidity scores. Thirty-day in-hospital mortality was higher in those with a NT-proBNP test (8.8%) vs. no request (3.2%). NT-proBNP test level was prognostic in univariate - OR 2.87 (2.61, 3.15), and multivariate analyses - OR 1.40 (1.26, 1.56). Higher NT-proBNP levels predicted higher 30-day in-hospital mortality. Multivariable thirty-day in-hospital mortality was 3.8% (3.6%, 3.9%) for those without a test, increasing to 4.9% (4.7%, 5.2%) for ≥ 250 ng/L and 5.8% (5.8%, 6.3%) for ≥ 3000 ng/L. LOS was linearly related to the total number of procedures/services performed.

CONCLUSION

NT-proBNP is prognostic in emergency medical admissions. Downstream resource utilization differed following an NT-proBNP test; this may reflect different case complexity or the 'uncertainty' surrounding such admissions.

Hospital mortality and length of stay differences in emergency medical admissions related to 'on-call' specialty

BACKGROUND

The outcomes of acute medical admissions have been shown to be influenced by a variety of factors including system, patient, societal, and physician-specific differences.

AIM

To evaluate the influence of on-call specialty on outcomes in acute medical admissions.

METHODS

All acute medical admissions to our institution from 2015 to 2020 were evaluated. Admissions were grouped based on admitting specialty. Thirty-day in-hospital mortality and length of stay (LOS) were evaluated. Data was analysed using multivariable logistic regression and truncated Poisson regression modelling.

RESULTS

There were 50,347 admissions in 30,228 patients. The majority of admissions were under Acute Medicine (47.0%), and major medical subspecialties (36.1%); Elderly Care admitted 12.1%. Acute Medicine admissions were older at 72.9 years (IQR 57.0, 82.9) vs. 67.2 years (IQR 50.1, 80.2), had higher Acute Illness Severity (grades 4-6: 85.9% vs. 81.3%; p < 0.001), Charlson Index (> group 0; 61.5% vs. 54.6%; p < 0.001), and Comorbidity Score (40.7% vs. 36.7%; p < 0.001). Over time, there was a small (+ 8%) but significant increase in 30-day in-hospital mortality. Mortality rates for Acute Medicine, major medical specialties, and Elderly Care were not different at 5.1% (95% CI: 4.7, 5.5), 4.7% (95% CI: 4.3, 5.1), and 4.7% (95% CI: 3.9, 5.4), respectively. Elderly Care admissions had shorter LOS (7.8 days (95% CI: 7.6, 8.0)) compared with either Acute Medicine (8.7 days (95% CI: 8.6, 8.8)) or major medical specialties (8.7 days (95% CI: 8.6, 8.9)).

CONCLUSION

No difference in mortality and minor differences in LOS were observed. The prior pattern of improved outcomes year on year for emergency medical admissions appears ended.

Parameters of the complete blood count predict in hospital mortality

INTRODUCTION

Mortality rates are used to evaluate the quality of hospital care after adjusting for disease severity and, commonly also, for age, comorbidity, and laboratory data with only few parameters of the complete blood count (CBC).

OBJECTIVE

To identify the parameters of the CBC that predict independently in-hospital mortality of acutely admitted patients.

POPULATION

All patients were admitted to internal medicine, cardiology, and intensive care departments at the Laniado Hospital in Israel in 2018 and 2019.

VARIABLES

Independent variables were patients' age, sex, and parameters of the CBC. The outcome variable was in-hospital mortality.

ANALYSIS

Logistic regression. In 2018, we identified the variables that were associated with in-hospital mortality and validated this association in the 2019 cohort.

RESULTS

In the validation cohort, a model consisting of nine parameters that are commonly available in modern analyzers had a c-statistics (area under the receiver operator curve) of 0.86 and a 10%-90% risk gradient of 0%-21.4%. After including the proportions of large unstained cells, hypochromic, and macrocytic red cells, the c-statistic increased to 0.89, and the risk gradient to 0.1%-29.5%.

CONCLUSION

The commonly available parameters of the CBC predict in-hospital mortality. Addition of the proportions of hypochromic red cells, macrocytic red cells, and large unstained cells may improve the predictive value of the CBC.

Association of the Charlson index with risk classification, clinical aspects, and emergency outcomes

Abstract Objective: To exam the association of the age-adjusted Charlson comorbidity index with the categories of risk classification, the clinical aspects, and the patient outcomes in the emergency department. Method: Cross-sectional, analytical study that analyzed the medical records of 3,624 patients seen in the emergency department. Charlson index scores greater than 2 showed a high rate of comorbidity (mortality risk). T-test and analysis of variance were applied in the analyses. Results: There was a significant difference between the Charlson comorbidity index and the risk classification, with higher scores found in patients classified in the white (2.57) and red (2.06) categories. Patients with vascular, endocrine, neurological, cardiologic, or device problems, and those who underwent a head tomography had a high rate of comorbidity. In addition, those admitted, transferred, or who died in the emergency room had significantly higher index scores compared to those who were discharged from the hospital. Conclusion: The high rate of comorbidity was associated with the categories of risk classification, main and nonspecific complaints, performance of a head tomography, and patient outcomes in the emergency room.

Machine learning techniques for mortality prediction in emergency departments: a systematic review

OBJECTIVES

This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).

DESIGN

A systematic review was performed.

SETTING

The databases including Medline (PubMed), Scopus and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilising vital sign variables to predict in-hospital mortality for patients admitted at EDs. Critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the prediction risk of bias assessment tool.

PARTICIPANTS

Admitted patients to the ED.

MAIN OUTCOME MEASURE

In-hospital mortality.

RESULTS

Fifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbours, support vector machine, gradient boosting, random forest, artificial neural networks and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction.

CONCLUSION

This review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.

Association of the All-Patient Refined Diagnosis-Related Groups Severity of Illness and Risk of Mortality Classification with Outcomes

OBJECTIVES

Diagnosis-related groups (DRGs) is a patient classification system used to characterize the types of patients that the hospital manages and to compare the resources needed during hospitalization. The DRG classification is based on International Classification of Diseases diagnoses, procedures, demographics, discharge status, and complications or comorbidities and compares hospital resources and outcomes used to determine how much Medicare pays the hospital for each "product/medical condition." The All-Patient Refined DRG (APR-DRG) incorporated severity of illness (SOI) and risk of mortality (ROM) into the DRG system to adjust for patient complexity to compare resource utilization, complication rates, and lengths of stay.

METHODS

This study included 18,478 adult patients admitted to a tertiary care center in Lubbock, Texas during a 1-year period. We recorded the APR-DRG SOI and ROM and some clinical information on these patients, including age, sex, admission shock index, admission glucose and lactate levels, diagnoses based on International Classification of Diseases, Tenth Revision discharge coding, length of stay, and mortality. We compared the levels of SOI and ROM across this clinical information.

RESULTS

As the levels of SOI and ROM increase (which indicates increased disease severity and risk of mortality), age, glucose levels, lactate levels, shock index, length of stay, and mortality increased significantly (P < 0.001). Multiple logistic regression analysis demonstrated that each unit increase in ROM and SOI level was significantly associated with an 11.45 and a 10.37 times increase in the odds of in-hospital mortality, respectively. The C-statistics for the corresponding models are 0.947 and 0.929, respectively. When both ROM and SOI were included in the model, the magnitudes of increase in odds of in-hospital mortality were 5.61 and 1.17 times for ROM and SOI, respectively. The C-statistic is 0.949.

CONCLUSIONS

This study indicates that the APR-DRG SOI and ROM scores provide a classification system that is associated with mortality and correlates with other clinical variables, such as the shock index and lactate levels, which are available on admission.

Emergency medical admissions and COVID-19: impact on 30-day mortality and hospital length of stay

BACKGROUND

The COVID-19 pandemic has put considerable strain on healthcare systems.

AIM

To investigate the effect of the COVID-19 pandemic on 30-day in-hospital mortality, length of stay (LOS) and resource utilization in acute medical care.

METHODS

We compared emergency medical admissions to a single secondary care centre during 2020 to the preceding 18 years (2002-2019). We investigated 30-day in-hospital mortality with a multiple variable logistic regression model. Utilization of procedures/services was related to LOS with zero truncated Poisson regression.

RESULTS

There were 132,715 admissions in 67,185 patients over the 19-year study. There was a linear reduction in 30-day in-hospital mortality over time; over the most recent 5 years (2016-2020), there was a relative risk reduction of 36%, from 7.9 to 4.3% with a number needed to treat of 27.7. Emergency medical admissions increased 18.8% to 10,452 in 2020 with COVID-19 admissions representing 3.5%. 18.6% of COVID-19 cases required ICU admission with a median stay of 10.1 days (IQR 3.8, 16.0). COVID-19 was a significant univariate predictor of 30-day in-hospital mortality, 18.5% (95%CI: 13.9, 23.1) vs. 3.0% (95%CI: 2.7, 3.4)-OR 7.3 (95%CI: 5.3, 10.1). ICU admission was the dominant outcome predictor-OR 12.4 (95%CI: 7.7, 20.1). COVID-19 mortality in the last third of 2020 improved-OR 0.64 (95%CI: 0.47, 0.86). Hospital LOS and resource utilization were increased.

CONCLUSION

A diagnosis of COVID-19 was associated with significantly increased mortality and LOS but represented only 3.5% of admissions and did not attenuate the established temporal decline in overall in-hospital mortality.

Non-Specific Clinical Presentations are Not Prognostic and do not Anticipate Hospital Length of Stay or Resource Utilization

AIM

To investigate whether a specific (SP) or non-specific (NSP) clinical presentation, predicts prognosis and in-hospital resource utilization in emergency medical admissions.

METHODS

We studied admissions over 5 years (2015-2019) and classified the symptom presentation as SP or NSP. The predictive capacity of the NSP category was related to 30-day in-hospital mortality with a multivariable logistic regression model. Utilization of procedures/services was related to hospital length of stay (LOS) with zero truncated Poisson regression.

RESULTS

There were 39,776 admissions in 23,995 patients. A NSP occurred in 18.2% of our top 20 clinical presentations; the top five being shortness of breath (12.8%), 'unwell' (7.1%), collapse (4.1%), abdominal pain (3.6%) and headache (2.7%). Baseline demographic characteristics were similar and unrelated to type of presentation; the model adjusted mortality by SP 4.0% (95% CI: 3.8%, 4.2%) or NSP 3.9% (95% CI: 3.5%, 4.4%) was identical. LOS was a dependant quantitative function of procedures/services undertaken; for the top two presentations of shortness of breath (SP) or unwell (NSP) there was no relationship between a SP or NSP presentation and hospital utilization of procedures/services or LOS.

CONCLUSION

Our data suggest no utility for a categorisation of presentations as specific or non-specific in terms of provision of prognostic information nor as an indicator of the pattern of hospital investigation or LOS.

A simple index predicting mortality in acutely hospitalized patients

BACKGROUND

Mortality rates used to evaluate and improve the quality of hospital care are adjusted for comorbidity and disease severity. Comorbidity, measured by International Classification of Diseases codes, do not reflect the severity of the medical condition, that requires clinical assessments not available in electronic databases, and/or laboratory data with clinically relevant ranges to permit extrapolation from one setting to the next.

AIM

To propose a simple index predicting mortality in acutely hospitalized patients.

DESIGN

Retrospective cohort study with internal and external validation.

METHODS

The study populations were all acutely admitted patients in 2015-16, and in January 2019-November 2019 to internal medicine, cardiology and intensive care departments at the Laniado Hospital in Israel, and in 2002-19, at St. James Hospital, Ireland. Predictor variables were age and admission laboratory tests. The outcome variable was in-hospital mortality. Using logistic regression of the data in the 2015-16 Israeli cohort, we derived an index that included age groups and significant laboratory data.

RESULTS

In the Israeli 2015-16 cohort, the index predicted mortality rates from 0.2% to 32.0% with a c-statistic (area under the receiver operator characteristic curve) of 0.86. In the Israeli 2019 validation cohort, the index predicted mortality rates from 0.3% to 38.9% with a c-statistic of 0.87. An abbreviated index performed similarly in the Irish 2002-19 cohort.

CONCLUSIONS

Hospital mortality can be predicted by age and selected admission laboratory data without acquiring information from the patient's medical records. This permits an inexpensive comparison of performance of hospital departments.

Is there excessive troponin testing in clinical practice? Evidence from emergency medical admissions

AIM

To investigate whether excessive high-sensitivity cardiac troponin T (hscTnT) testing, in non-cardiac presentations, increases hospital length of stay (LOS) by driving down-stream investigations.

METHODS

We report on all hscTnT tests in emergency medical admissions, performed over a 9-year period between 2011-2019. Troponin testing frequency in different risk cohorts was determined and related to 30-day in-hospital mortality with a multivariable logistic regression model adjusted for other outcome predictors. Downstream utilization of procedures/services was related to LOS with zero truncated Poisson regression.

RESULTS

There were 66,475 admissions in 36,518 patients. hscTnT was tested in 24.4% of admissions, more frequently in the elderly (>70 years 33.4%, >80 years 35.9%), cardiovascular presentations (33.6%) and in those with high comorbidity (42.2%), and reduced in those with neurologic presentations (20%). A hscTnT request predicted increased 30-day in-hospital mortality OR 3.33 (95% CI: 3.06, 3.64). The univariate odds ratio (OR) of hscTnT test result was 1.45 (95% CI: 1.42, 1.49) and was semi-quantative with worsening outcomes as hscTnT increased. It remained predictive in the fully adjusted model OR 1.17 (95% CI: 1.09, 1.26). LOS was linearly related to the number of procedures/services performed. hscTnT testing did not increase LOS or number of procedures/services CONCLUSION: : A clinical request for hscTnT testing is prognostic and risk categorises. Subsequent resource utilization, if increased, appears an epiphenomenon related to risk categorisation, rather than being driven by inappropriate hscTnT testing.

Regional Disparity of Medical Resources and Its Effect on Mortality Rates in China

Objectives: The purpose of this study was two-fold. First, to empirically study the effects that medical resources (i.e., hospital, doctors, beds) have on the mortality rate in China. Second, to divide China into east, middle, and west regions, and empirically study the regional disparity of medical resources and its effect on mortality rates in China. Methodology and Data: This study utilized a panel data regression model to explore the effect medical resources have on the age-standardized mortality rate in China. The data came from the 2003-2017 China Statistical Yearbook compiled by the National Bureau of Statistics of China. Results: Nationwide, hospitals, doctors, and beds had a significant negative correlation with the mortality rate. In the western region, hospitals, beds, and doctors had a significant negative correlation with the mortality rate. In China's middle and eastern regions, hospitals, beds, and doctors had no significant effect on the mortality rate. In China, increased hospitals, doctors, and beds significantly reduced the mortality rate. The distribution of medical resources in eastern, middle, and western China was unequal. More hospitals, beds, and doctors in the less developed western regions can more effectively alleviate the local mortality rate. In the middle and east regions, hospitals, beds, and doctors had no significant impact on the local mortality rate. Conclusion: First, China's overall medical resources are still inadequate and improving medical resources throughout the country could reduce the mortality rate. Second, due to the imbalanced distribution of medical resources in China, the Chinese government should implement more supportive policies for medical resources in the western region. At the same time, we should also actively develop the western region by improving local per capita GDP and reducing unemployment, so as to fundamentally reduce the local mortality rate.