scholarly journals Outcomes Following Acute Pulmonary Embolism in an Irish Population of over 1 Million Individuals: Opportunities for Quality Improvement and More Effective Resource Utilisation

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2979-2979
Author(s):  
Alexandra Tierney ◽  
Fionnuala Ni Ainle ◽  
Declan Lyons ◽  
Osasere Edebiri ◽  
Khalid Saeed ◽  
...  

Abstract Introduction Pulmonary embolism (PE) is a leading cause of cardiovascular morbidity worldwide. The risk of early death in the setting of untreated PE may be as high as 30%. However, diagnostic and therapeutic advances in recent years have led to a progressive decline in global PE-related mortality and recent data describing rates of in-hospital death following PE suggest a mortality rate of approximately 5-15%. Moreover, strategies directed at stratification of PE severity have been shown to safely identify a sub-group of low-risk patients (up to 30-50% of all patients) for whom outpatient management is feasible without the need for hospital admission. Avoiding hospitalisation for low-risk PE patients is associated with improved patient satisfaction and avoids exposing patients to the risks associated with hospital admission. Ambulatory PE management would also be predicted to lead to significant healthcare cost-savings. However ambulatory care models for low-risk PE appear to be under-utilised despite these potential benefits. Barriers to implementation include access to outpatient follow-up services and the perceived risks associated with this model of care. The Ireland East Hospital Group (IEHG) is the largest hospital network in the Republic of Ireland, consisting of 11 hospitals (including large academic centres, community general hospitals and the national maternity hospital). The IEHG serves a population of over 1.1 million individuals. We sought to determine the frequency of admissions to hospital with PE and to assess key outcomes, including length-of-stay (LOS) and in-hospital mortality within this population. Methods Data pertaining to PE diagnosis from January 2018 to December 2020 were obtained from NQAIS Clinical (National Quality Assessment and Improvement System; an electronic reporting tool which is populated with anonymised data extracted from the hospital in-patient enquiry system). This system compiles diagnostic data on all patients by ICD-10 code at the time of discharge. For the purposes of this analysis the ICD-10 codes I26.0 and I26.9 were used to identify patients with PE and only admission episodes where PE was the primary diagnosis were included; cases of 'secondary PE' (historical PE or hospital-acquired) were excluded. Projected population figures, extrapolated from Census 2016 data, were obtained from Health Atlas Ireland (an open-source application providing access to datasets developed by the Health Intelligence Unit of the Health Service Executive of Ireland). Results During the 3-year study period, 958 in-patient episodes occurred where PE was recorded as the primary diagnosis, corresponding to an incidence of 0.37 per 1000 adults per annum (95% CI 0.35 to 0.40). The incidence was highest in the over 85 years age-group (1.07 per 1000 per annum; 95% CI 0.80 to 1.33). PE was more common in women in all age-groups apart from the 46-65 years age group [males: 0.51 (95% CI 0.44-0.51) vs females: 0.36 (95% CI 0.3-0.42) per 1000]. In 82.7% of episodes, the ultimate discharge destination was to home. In 5.3% the discharge destination was a nursing home and 4.6% were transferred to another hospital. The all-cause in-hospital mortality rate was 3.1% (30 fatalities; 18 females, 12 males). Most deaths occurred in the 66-85 years age-group (n=14), with 9 fatalities in the age >85 years group and 7 fatal PE events in the 46-65 years age-group. Average hospital LOS was 7.8 days. 8.9% of inpatient episodes resulted in same-day discharge. In 55.9% of episodes, discharge occurred after day 4. Those discharged to home had an average length of stay of 6.31 days, while patients awaiting nursing home facilities averaged 26.5 days. Conclusion The incidence of acute presentation with PE within this population is consistent with international reports. The rate of in-hospital mortality compares favourably with these international standards. The mortality rate may reflect improvements in PE care but may also reflect the inclusion of a significant number of 'low-risk' individuals in the analysis (many of whom may have been suitable for outpatient management). The mortality rate might also reflect increased detection of small, low-risk distal PE (as a result of advances in diagnostics). In any event, these data suggest that more widespread implementation of outpatient PE management is likely to be feasible and would represent an opportunity for improved resource utilisation. Disclosures Ni Ainle: Leo Pharma: Research Funding; Actelion: Research Funding; Daiichi-Sankyo: Research Funding; Bayer Pharma: Research Funding. Kevane: Leo Pharma: Research Funding.

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 747-747
Author(s):  
Craig I Coleman ◽  
Christine G Kohn ◽  
Concetta Crivera ◽  
Jeff Schein ◽  
W Frank Peacock

Background: Current guidelines suggest that low risk pulmonary embolism (PE) patients may be managed as outpatients or with an abbreviated hospital stay. There is need for a claims-based prediction rule that payers and hospitals can use to efficiently risk stratify PE patients. The authors recently derived a rule found to have high sensitivity and moderate specificity for predicting in-hospital mortality. Objective: To validate the In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT) prediction rule originally developed in a commercial claims database in an all-payer administrative database restricted to inpatient claims. Methods: This study utilized data from the 2012 Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS). Adult PE admissions were identified by the presence of an appropriate International Classification of Diseases, ninth edition, Clinical Modification (ICD-9-CM) code either in the primary position or secondary position when accompanied by a primary code for a PE complication. The IMPACT rule, consists of age + 11 weighted comorbidities calculated based upon the maximum of 25 ICD-9-CM diagnosis codes and 25 procedural codes reported for each discharge in the NIS (myocardial infarction, chronic lung disease, stroke, prior major bleeding, atrial fibrillation, cognitive impairment, heart failure, renal failure, liver disease, coagulopathy, cancer), and was used to estimate patients' risk of in-hospital mortality. Low risk was defined as in-hospital mortality ≤1.5%. We present the validity of the rule by calculating prognostic test characteristics and 95% confidence intervals (CIs). In order to estimate the potential cost savings from an early discharge, we calculated the difference in total hospital costs between low-risk patients having and not having an abbreviated hospital stay (defined as ≤1, ≤2 or ≤3 days). Results: A total of 34,108 admissions for PE were included (46.7% male, mean ± standard deviation age of 61.9±17.2); and we observed a 3.4% in-hospital PE case-fatality rate. The IMPACT prediction rule classified 11,025 (32.3%) patient admissions as low-risk; and had a sensitivity of 92.4% (95%CI=90.7-93.8), specificity of 33.2% (95%CI=32.7-33.7), negative and positive predictive values of 99.2% (95%CI=99.0-99.4) and 4.6% (95%CI=4.4-4.9) and a C-statistic of 0.74 (95%CI=0.73-0.76) for in-hospital mortality. Low-risk patients had significantly lower in-hospital mortality (0.8% vs. 4.6%, odds reduction of 83%; 95%CI=79-87), shorter LOSs (-1.2 days, p<0.001) and lower total treatment costs (-$3,074, p<0.001) than patients classified as higher-risk. Of low-risk patients, 13.1%, 31.1% and 47.7% were discharged within 1, 2 and 3 days of admission. Low-risk patients discharged within 1 day accrued $5,465 (95%CI=$5,018-$5,911) less in treatment costs than those staying longer. Discharge within 2 or 3 days in low-risk patients was also associated with a reduced cost of hospital treatment [$5,820 (95%CI=$5,506-$6,133) and $6,314 (95%CI=$6,031-$6,597), respectively] when compared to those staying longer. Conclusion: The prior claims-based in-hospital mortality prediction rule was valid when used in this all-payer, inpatient only administrative claims database. The rule classified patients' mortality risk with high sensitivity and had a high negative predictive value; and consequently, may be valuable to those wishing to benchmark rates of PE treated at home or following an abbreviated hospital admission. Disclosures Coleman: Janssen Scientific Affairs, LLC: Consultancy, Research Funding. Crivera:Janssen Scientific Affairs, LLC: Employment, Equity Ownership. Schein:Janssen Scientific Affairs, LLC: Employment. Peacock:Singulex: Consultancy; Prevencio: Consultancy; The Medicines Company: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Portola: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy, Research Funding; Cardiorentis: Research Funding; Banyan: Research Funding; Alere: Research Funding; Abbott: Research Funding; Comprehensive Research Associates, LLC: Equity Ownership; Emergencies in Medicine, LLC: Equity Ownership.


2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Shabnam Nasserifar ◽  
Kam Sing Ho

Abstract PURPOSE: To determine the relationship between diabetes and thirty-days readmission, mortality, morbidity, and health care resource utilization in patients who were admitted with ST-Elevation Myocardial Infarction (STEMI) in the United States. METHOD: A retrospective study was conducted using the AHRQ-HCUP Nationwide Readmission Database for the year 2014. Adults (≥ 18 years) with a primary diagnosis of STEMI (1), along with a secondary diagnosis of diabetes were identified using ICD-9 codes as described in the literature (2). The primary outcome was the rate of all-cause readmission within 30 days of discharge. Secondary outcomes were reasons for readmission, readmission mortality rate, morbidity, and resource use (length of stay and total hospitalization costs and charges). Propensity score (PS) using the 1:1 nearest neighbor matching without replacement was utilized to adjust for confounders (3). Independent risk factors for readmission were identified using a Cox proportional hazards model (4). RESULTS: In total, 116,124 hospital admissions among adults with a primary diagnosis of STEMI were identified, of which 18.05% were diabetics. 1:1 PS matching was performed based on demographic (age, gender, hospital status, etc.) and clinical characteristics (Charlson comorbidity score. The 30-day rate of readmission among diabetics and non-diabetics with STEMI were 9.31% vs. 6.18% (p &lt;0.001). The most common readmission for both groups was recurrent myocardial infarction. During the index admission for STEMI, the length of stay (LOS) among diabetics and non-diabetics patients were not statistically different (4.74 vs 4.58 days, p=0.12). However, the total hospital cost for the diabetic patients was statistically different ($27,027 vs $24,807, p &lt;0.001). Most importantly, diabetics patients’ in-hospital mortality rate during their index admission was significant higher (10.20% vs 5.92%, p &lt;0.001). Amongst those readmitted, the LOS, total hospital cost, or in-hospital mortality among diabetics were not statistically different when compared to their counterparts during their readmission. Diabetes (HR 1.60, CI 1.27-2.02, p &lt;0.001) was an independent predictor associated with higher risks of readmission. Other independent predictors associated with increased 30-day readmission include acute exacerbation of CHF, acute exacerbation of COPD, acute kidney injury, secondary diagnosis of pneumonia, history of COPD, history of ischemic stroke, history of atrial fibrillation & atrial flutter, history of chronic kidney disease, history of iron deficiency, and use of mechanical ventilator. CONCLUSION: In this study, diabetics patients admitted with STEMI have a higher 30 days of readmission rate, total hospital cost, and in-hospital mortality (p &lt;0.001) than their non-diabetic counterparts.


2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Önsel Öner ◽  
Figen Deveci ◽  
Selda Telo ◽  
Mutlu Kuluöztürk ◽  
Mehmet Balin

Summary Background The aim of this study was to determine levels of Mid-regional Pro-adrenomedullin (MR-proADM) and Mid-regional Pro-atrial Natriuretic Peptide (MR-proANP) in patients with acute pulmonary embolism (PE), the relationship between these parameters and the risk classification in addition to determining the relationship between 1- and 3-month mortality. Methods 82 PE patients and 50 healthy control subjects were included in the study. Blood samples for MR-proANP and MR-proADM were obtained from the subjects prior to the treatment. Risk stratification was determined according to sPESI (Simplified Pulmonary Embolism Severity Index). Following these initial measurements, cases with PE were assessed in terms of all causative and PE related mortalities. Results The mean serum MR-proANP and MR-proADM levels in acute PE patients were found to be statistically higher compared to the control group (p < 0.001, p < 0.01; respectively) and statistically significantly higher in high-risk patients than low-risk patients (p < 0.01, p < 0.05; respectively). No statistical difference was determined in high-risk patients in case of sPESI compared to low-risk patients while hospital mortality rates were higher. It was determined that the hospital mortality rate in cases with MR-proANP ≥ 123.30 pmol/L and the total 3-month mortality rate in cases with MR-proADM ≥ 152.2 pg/mL showed a statistically significant increase. Conclusions This study showed that MR-proANP and MR-proADM may be an important biochemical marker for determining high-risk cases and predicting the mortality in PE patients and we believe that these results should be supported by further and extensive studies.


1975 ◽  
Vol 3 (1) ◽  
pp. 65-67
Author(s):  
J. C. Clubb ◽  
G. M. Stathers

The results of a two-year prospective study of myocardial infarction in a rural city is reported. In the under 70 years age group there was a 10 per cent mortality of patients whilst being nursed in the coronary care area. Later deaths after transfer to the general wards raised the overall hospital mortality rate to 15 per cent. These figures compare favourably with other series.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 7-8
Author(s):  
Lana Mucalo ◽  
Amanda M. Brandow ◽  
Sadie F. Mason ◽  
Ashima Singh ◽  
Bradley W. Taylor ◽  
...  

Sickle cell disease (SCD) is an inherited hemoglobinopathy that can affect nearly every organ system. Individuals living with SCD are at high risk of developing serious infections which can further trigger disease related complications and attribute additional morbidity and mortality. In light of the evolving pandemic caused by SARS-CoV-2, the causative agent of COVID-19 disease, and the potential for future infectious disease epidemics, it is important to understand the impact that COVID-19 has on hospitalization rates and mortality in this medically vulnerable population. The objective of this study was to describe hospitalization and case fatality rates secondary to COVID-19 among individuals living with SCD in different age groups and compare these to the general population. The Medical College of Wisconsin established the international SECURE-SCD Registry to collect data on pediatric and adult COVID-19 infections in individuals living with SCD. Providers are instructed to report confirmed COVID-19 cases to the registry after sufficient time has passed to observe the disease course through resolution of acute illness and/or death. For each case, providers complete a short form that includes the following data: patient demographics, COVID-19 related hospitalization, COVID-19 severity/management strategies, if the patient died due to COVID, and other information about SCD complications. Data are de-identified and without protected health information to facilitate rapid and increased reporting. We calculated the hospitalization rate and case fatality rate for individuals with SCD by specific age group and contrasted it with the rates publicly available for the general Black population. We utilized data from California Department of Public Health for case fatality rate comparison in Blacks and data from COVID-NET for hospitalization rate comparison. We used indirect age adjustment to calculate standardized mortality ratios using COVID-19 data from California state as the reference population. As of July 17th 2020, 218 cases of COVID-19 in Blacks with SCD in the US were reported to the registry. There was a slight predominance of females (52.8%) and 32.1% of reported cases were patients 18 years and under. There were 15 deaths reported with overall mortality rate of 6.9%. Figure 1 shows the distribution of cases and deaths by age group and gender. Mortality rate in SCD patients was highest in the 50-64 years age group (23.1%) in contrast to mortality rate peaks seen in the general population in patients older than 80 years (Table 1). Young adult SCD patients aged 18-34 years had a case fatality rate of 3.3% and those aged 34-50 years had a rate of 14.9%. California Department of Public Health report case fatality rates for Blacks are less than 1% in both of these comparative age groups. Age-standardized mortality ratio shows that individuals with SCD are 7.7 times more likely to die due to COVID-19 infection compared to the general population. The overall hospitalization rate in individuals with SCD was 72.5% and 18.8% of reported hospitalized cases were children. Among hospitalized adults with SCD, stratification by age showed that 85% were aged 18-49, whereas only 25.7% of people 18-49 years in the general Black population were hospitalized (Table 2). Our findings show that individuals with SCD who have COVID-19 infection have higher rates of death due to COVID-19 than the general Black population. Also, a large proportion of COVID hospitalization for the SCD population occurs among the younger age group. Further analysis is planned to examine effects of underlying comorbidities and prior SCD-associated complications on the severity of COVID-19 in individuals with SCD. Disclosures Mucalo: NIH/NHLBI: Research Funding; NIH/NINDS: Research Funding. Brandow:Greater Milwaukee Foundation: Research Funding; NIH / NHLBI: Research Funding. Panepinto:HRSA: Research Funding; NINDS: Research Funding; NINDS: Research Funding; NHLBI: Research Funding.


2007 ◽  
Vol 39 (4) ◽  
pp. 901-903 ◽  
Author(s):  
H. Khedmat ◽  
H. Araghizadeh ◽  
S. Assari ◽  
M. Moghani-Lankarani ◽  
M. Aghanassir

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 533-533
Author(s):  
Neela K Kumar ◽  
Erin R Weeda ◽  
Philip S Wells ◽  
Frank Peacock ◽  
Gregory J Fermann ◽  
...  

Abstract Background: Both the simplified Pulmonary Embolism Severity Index (sPESI) and the multivariable In-hospital Mortality for Pulmonary embolism using Claims daTa (IMPACT) rule classify patients' risk of early post-pulmonary embolism (PE) complications. Objective: To externally validate sPESI and IMPACT for predicting 90-day all-cause mortality and readmission rates among PE patients treated within the Veterans Health Administration (VHA). Methods: We used VHA data from 10/1/2010-9/30/2015 to identify adult patients with: (1) ≥1 inpatient diagnosis for acute PE (International Classification of Diseases-9th Revision-Clinical Modification codes=415.1x), (2) continuous medical and pharmacy enrollment for ≥12-months prior to the index PE (baseline period), (3) a minimum of 90-days of post-event follow-up or until death (whichever came first), and (4) ≥1 claim for an anticoagulant during the index PE stay. Patients were excluded if they had a claim for PE or an anticoagulant during the baseline period. We classified patients as low-risk for early post-PE complications if their sPESI score=0 or their absolute in-hospital mortality risk estimated by IMPACT was <1.5% (the latter calculated using the formula: 1/(1 + exp(-x); where x = −5.833 + [0.026*age] + [0.402*myocardial infarction] + [0.368*chronic lung disease] + [0.464*stroke] + [0.638*prior major bleeding] + [0.298*atrial fibrillation] + [1.06 1*cognitive impairment] + [0.554*heart failure] + [0.364*renal failure] + [0.484*liver disease] + [0.523*coagulopathy] + [1.068*cancer]). Sensitivity, specificity, negative and positive predictive value (NPV and PPV) for all-cause mortality, all-cause readmission, and readmission for recurrent venous thromboembolism (VTE) or major bleeding at 90-days were reported with 95% confidence intervals (CIs) for sPESI and IMPACT tools. Results: Of6,746 eligible PE patients, 851 (12.6%) died and 1,359 (20.1%) were readmitted for any reason within 90-days. Hospitalization for recurrent VTE and major bleeding occurred in 375 (5.6%) and 116 (1.7%), respectively.sPESI classified 1,918 (28.4%) as low-risk, while 1,024 (15.2%) were low-risk per IMPACT. Both tools displayed sensitivity >90% and NPVs >96% for all-cause 90-day mortality, but low specificity and PPVs (Table). IMPACT's sensitivity for all-cause readmission was numerically higher than sPESI, but both had comparable NPVs. Similar trends were observed for accuracy in predicting readmissions due to recurrent VTE or major bleeding. Conclusion: In this external validation study utilizing VHA data, IMPACT classified patients for 90-day post-PE outcomes with similar accuracy as sPESI. While not recommended for prospective clinical decision-making, IMPACT appears useful for identification of PE patients at low-risk for early mortality or readmission in retrospective claims-based studies. Table. Test characteristics for sPESI and IMPACT for 90-day post-pulmonary embolism outcomes CI= confidence interval; IMPACT=In-hospital Mortality for Pulmonary embolism using Claims data; NPV=negative predictive value; PPV=positive predictive value; sPESI=simplified Pulmonary Embolism Severity Index; VTE=venous thromboembolism Table. Test characteristics for sPESI and IMPACT for 90-day post-pulmonary embolism outcomes CI= confidence interval; IMPACT=In-hospital Mortality for Pulmonary embolism using Claims data; NPV=negative predictive value; PPV=positive predictive value; sPESI=simplified Pulmonary Embolism Severity Index; VTE=venous thromboembolism Disclosures Kumar: Johnson & Johnson: Employment. Wells:Itreas: Other: Served on a Writing Committee; Janssen Pharmaceuticals: Consultancy; Bayer Healthcare: Other: Speaker Fees and Advisory Board; BMS/Pfizer: Research Funding. Peacock:Comprehensive Research Associates LLC: Equity Ownership; Cardiorentis: Consultancy, Research Funding; The Medicine's Company: Consultancy, Research Funding; Banyan: Research Funding; Emergencies in Medicine LLC: Equity Ownership; Abbott: Research Funding; Alere: Consultancy, Research Funding; Prevencio: Consultancy; Janssen: Consultancy, Research Funding; Portola: Consultancy, Research Funding; Pfizer: Research Funding; Roche: Research Funding; ZS Pharma: Consultancy, Research Funding; Ischemia Care: Consultancy; Phillips: Consultancy. Fermann:Janssen Pharmaceuticals: Other: Advisory Board, Speakers Bureau; Pfizer: Research Funding. Wang:Janssen Pharmaceuticals: Research Funding. Baser:Janssen Pharmaceuticals: Research Funding. Schein:Johnson & Johnson: Employment, Equity Ownership, Other: Own in excess of $10,000 of J&J stock. Crivera:Johnson & Johnson: Employment, Equity Ownership, Other: Owns excess of $10,000 in stock. Coleman:Boehringer-Ingelheim Pharmaceuticals, inc.: Consultancy, Research Funding; Bayer Pharmaceuticals AG: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy, Research Funding.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chingching Foocharoen ◽  
Udomlack Peansukwech ◽  
Patnarin Pongkulkiat ◽  
Ajanee Mahakkanukrauh ◽  
Siraphop Suwannaroj

AbstractOccupational and environmental associations with systemic sclerosis (SSc) have been confirmed; however, the association between aerosol components and mortality is uncertain. The study aimed to define the association between aerosol components and hospital mortality among Thai SSc patients. A study was conducted using a national database of patients covered by the National Health Security Office, hospitalised between 2014 and 2018. Data included all patients over 18 having a primary diagnosis of SSc (ICD-10: M34). Spatial resources used map information based on GPS coordinates of Thailand. Aerosol components—including organic carbon, black carbon, dust particulate matter diameter < 2.5 µm (PM2.5), and sulfate—were assessed using the NASA satellite MERRA-2 Model M2TMNXFLX v5.12.4. Spatial modelling with R Package Integrated Nested Laplace Approximation (R-INLA) was used to analyse the association between the incidence of mortality and the 5-year accumulation of each aerosol component adjusted by age, sex, and comorbid diseases. The study included 2,094 SSc patients with 3,684 admissions. Most (63.8%) were female. During admission, 1,276 cases died. R-INLA analysis indicated an increase of 1 µg/m3 of dust PM2.5 was associated with a respective increase in the risk of overall mortality and death due to pneumonia of 96% and 79%. An increase of 1 µg/m3 of dust PM2.5 resulted in 1.17, 1.18, 1.64, and 2.15 times greater risk of mortality due to pulmonary fibrosis, cardiac involvement, renal involvement, and cancer, respectively. Aerosol components—particularly dust PM2.5 exposures—increased the risk of overall, cardio-pulmonary-renal, and cancer mortality among SSc patients.


2021 ◽  
Vol 17 (1) ◽  
pp. 40-46
Author(s):  
Magdalena Walicka ◽  
Monika Puzianowska-Kuznicka ◽  
Marcin Chlebus ◽  
Andrzej Śliwczyński ◽  
Melania Brzozowska ◽  
...  

IntroductionMortality, whether in or out of hospital, increases with age. However, studies evaluating in-hospital mortality in large populations did not distinguish between surgical and non-surgical causes of death, either in young or in elderly patients. The aim of the study was to assess in-hospital non-surgical mortality in a large group of patients, with a special focus on the elderly.Material and methodsData from the database of the Polish National Health Fund (NHF) regarding hospitalizations of adult (≥ 18 years) patients not related to surgical procedures in the years 2009–2013 were used to assess in-hospital mortality.Results15,345,025 hospitalizations were assessed. The mean in-hospital non-surgery-related mortality rate was 3.96 ±0.17%, and increased from 3.79% to 4.2% between 2009 and 2013. The mean odds ratio for in-hospital death increased with the age of patients, reaching a 229-fold higher rate in the ≥ 95 years age group as compared to the 18–24 age group. The highest mean mortality was associated with respiratory diseases (6.91 ±0.20%), followed by heart and vascular diseases, nervous system diseases, as well as combined gastrointestinal tract, liver, biliary tract, pancreas and spleen diseases (5.65 ±0.27%, 5.46 ±0.05% and 4.01 ±0.13%, respectively).ConclusionsThe in-hospital non-surgery-related mortality rate was approximately 4%. It significantly increased with age and, regardless of age, was highest in patients suffering from respiratory diseases.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2058-2058
Author(s):  
Christine G Kohn ◽  
Heather Rozjabek ◽  
Gregory J Fermann ◽  
W Frank Peacock ◽  
Concetta Crivera ◽  
...  

Abstract Background: Previous studies evaluating the simplified Pulmonary Embolism Severity Index (sPESI) for predicting pulmonary embolism (PE) mortality did not consistently report the timing of vital sign measurement (systolic blood pressure [SBP], heart rate [HR] and oxygen [O2] saturation) relative to the PE presentation. Objectives: To evaluate the impact of vital sign measurement timing on sPESI's ability to identify PE patients at low-risk for in-hospital all-cause mortality. Methods: This was a retrospective analysis of PE patients from a large, urban teaching hospital in the Northeastern United States. Consecutive patients, diagnosed with PE between November 2010 and May 2015, were identified using the institution's billing system. To be eligible for inclusion, patients had an International Classification of Diseases, ninth-revision, clinical modification (ICD-9-CM) code of 415.1x in the primary position. Those in whom PE could not be objectively confirmed via chart review and those receiving thrombolysis or embolectomy were excluded. Patients' first and either lowest (SBP, O2 saturation) or highest (HR) value within the first 24 hours from presentation (subsequently referred to as "least favorable" values) were recorded. We then compared sensitivity, specificity and negative predictive values (NPV) and 95% confidence intervals (CIs) and the ability of the sPESI to predict all-cause in-hospital mortality using the first and least favorable vital signs. Results: A total of 562 PE patients (18.9% >80 years of age, 28.5% history of cardiopulmonary disease, 29.5% history of cancer) were included and 2.1% died in-hospital. No differences in sPESI's sensitivity, specificity or NPV were observed when scored using the first or least favorable vital sign values. sPESI classified 169 (30.1%) as low-risk (sPESI=0) vs. 153 (27.2%) when the least favorable vital sign value was used. Conclusions: The sensitivity and NPV of sPESI to predict PE patients' risk for all-cause in-hospital mortality is not affected by the timing of vital sign measurement. Using the least favorable value within 24-hours of presentation does result in a smaller proportion of patients being classified as low-risk. Table 1.CharacteristicFirst% (95%CI)Least Favorable% (95%CI)P-valueSensitivity91.7 (59.8-99.6)91.7% (59.8-99.6)>0.99Specificity30.5 (26.8-34.6)27.6% (24.0-31.6)0.31NPV99.4 (96.2-99.9)99.3% (95.9-99.9)0.94Proportion classified as low-risk, n (%)169 (30.1)153 (27.2)<0.001Comparison of the Ability of sPESI to Predict All-Cause In-Hospital Mortality using the First and Least Favorable Recorded Vital SignsCI=confidence interval; n=number; NPV=negative predictive value Disclosures Rozjabek: Janssen Scientific Affairs, LLC: Other: Internship. Fermann:Janssen Pharmaceuticals: Consultancy, Speakers Bureau; Novartis: Research Funding; Cardiorentis: Research Funding; Cardioxyl: Research Funding; Cempra Pharmaceuticals: Research Funding; Trevena: Research Funding; Intersection Medical: Consultancy, Research Funding; Siemens: Research Funding; The Mayday Foundation: Research Funding; Pfizer: Research Funding. Peacock:Abbott: Research Funding; Alere: Research Funding; Banyan: Research Funding; Cardiorentis: Research Funding; Janssen Pharmaceuticals: Consultancy, Research Funding; Portola: Consultancy, Research Funding; Roche: Consultancy, Research Funding; The Medicines Company: Consultancy, Research Funding; Prevencio: Consultancy; Singulex: Consultancy; Comprehensive Research Associates, LLC: Equity Ownership; Emergencies in Medicine, LLC: Equity Ownership. Crivera:Janssen Scientific Affairs, LLC: Employment, Equity Ownership. Schein:Janssen Scientific Affairs, LLC: Employment. Coleman:Janssen Scientific Affairs, LLC: Consultancy, Research Funding.


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