Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Hospital Mortality”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2006

Characteristics of patients and predictors of in-hospital mortality after hospitalization for head and neck cancers.

BACKGROUND: The objectives of this study were to describe the characteristics of patients who were hospitalized for head and neck cancer (HNC) during the years 2000 through 2003 and to identify predictors of in-hospital mortality. METHODS: The Nationwide Inpatient Sample for the years 2000 through 2003 was used. All patients who had a primary diagnosis of any of the HNCs were included in the study. Univariate and multivariate logistic regression analyses were used to identify patient and hospital characteristics that were associated with in-hospital mortality. RESULTS: In total, 24,803 patients were hospitalized for HNCs. The average age of patients was 62 years, the mean length of stay in the hospital was 7.89 days, and the in-hospital mortality rate was 5.18%. Patients who had comorbid conditions and complications and patients who were grouped under the self-pay/no charge/others category had greater odds of in-hospital mortality compared with patients who were covered by private insurance (P<.02). Patients who were treated in large-bed, urban, or teaching hospitals had lower odds of in-hospital mortality compared with patients who were treated in small or medium-bed, rural, or nonteaching hospitals, respectively (P<.03). CONCLUSIONS: Patients with comorbid conditions and complications and patients without adequate insurance coverage had greater odds of in-hospital mortality. One reason for this may be inadequate access to care because of the absence of insurance or underinsurance. Further studies controlling for disease stage will be required to determine whether insurance status and patient-related factors can influence outcomes from HNC in individual patients independent of their disease stage.

Aged↗
PubMed2002

In-hospital stroke mortality, hospital transfers, and referral bias at a rural academic medical center.

A chart review of acute stroke (DRG 14) discharges from a rural academic medical center in 1999 was performed because of an observed high in-hospital crude acute stroke mortality rate. An analysis of the results of this chart review demonstrated that the odds ratio of intracerebral hemorrhage in patients who had been transferred from other hospitals was 11.7 compared to intracerebral hemorrhage in nonhospital transfer patients (p < 0.00001). This finding illustrates the potential magnitude and significance of referral bias at a rural academic medical center.

Academic Medical Centers↗
PubMed2003

Risk stratification of in-hospital mortality in patients hospitalized for chronic congestive heart failure secondary to non-ischemic cardiomyopathy.

The study population consisted of 234 consecutive patients hospitalized for acute exacerbation of congestive heart failure secondary to non-ischemic cardiomyopathy. Of the 234 patients, there were 55 in-hospital deaths. Their medical records were deliberatively reviewed and the association of 38 clinical, hemodynamic and biochemical variables with in-hospital mortality was evaluated by multiple stepwise logistic regression analysis. The following variables were statistically associated with in-hospital mortality: profound cardiogenic shock, severe hyponatremia, the presence of ventricular arrhythmias, history of stroke, the presence of acute renal failure, and requirement of dobutamine therapy. In stratified analyses, the rates of in-hospital mortality rose rapidly as the number of risk factors increased: 0 risk factors, 2.5%; 1 risk factor, 5.1%; 2 risk factors, 36.4%; 3 risk factors, 75%, and no less than 4 risk factors, 100%. In conclusion, our study identified 6 variables that correlated with in-hospital death in patients with heart failure secondary to non-ischemic cardiomyopathy. The identification of these variables may allow more accurate risk stratification of individuals at risk of in-hospital mortality in this clinical setting.

Adult↗
PubMed1998

Infection in surgical patients: effects on mortality, hospitalization, and postdischarge care.

In-hospital mortality, length of stay (LOS), and level of postdischarge care in infected and noninfected surgical patients were studied. An analysis was conducted of a database that included diagnostic, procedure, and drug data collected when surgical patients were discharged from the hospital. Hospitals consisted of 90 nongovernment, nonspecialty, teaching, and nonteaching acute care hospitals of more than 100 beds. Patients in the database included 288,906 patients of all ages hospitalized between July and September 1994. Patients selected of those who had undergone procedures likely to pose a moderate to high risk of infection. Of the 288,906 patients, 12,384 had undergone a moderate- to high-risk procedure; of these, 1,479 (11.9%) had had an infection during their hospitalization. Infection rates ranged from 1.9% to 25.4%, depending on the procedure. The in-hospital mortality rate in infected patients was 14.5%, versus 1.8% for noninfected patients. Similarly, LOS in infected patients (median, 14 days) was substantially greater than in noninfected patients (4 days). About 24% of infected patients required additional professional care after discharge, compared with 7% of noninfected patients. Infection occurs in a substantial portion of surgical patients and is associated with a higher death rate, longer hospitalization, and more intense postdischarge care.

Hospitalization↗
PubMed2000

HIV infection and in-hospital mortality at an academic hospital in South Africa.

AIM: To document the impact that rapid increases in HIV infection in hospitalised children at Chris Hani Baragwanath Hospital has had on in-hospital mortality. METHODS: Hospital discharge summaries from January 1992 to the end of 1996 were reviewed. RESULTS: There were 20 733 admissions in the five year period; 7985 (39%) were tested for HIV. In tested admissions above 15 months of age, 4.9% were HIV infected in 1992, increasing to 35% in 1996. Under 15 months of age, 9% of tested admissions were positive in 1992, increasing to 46% in 1996. The proportion of all hospital deaths occurring in children considered HIV infected (ELISA testing together with clinical features if 15 months or younger) increased from 6.7% in 1992 to 46. 1% in 1996 (p < 0.001). In-hospital mortality for all children increased by 21% from 4.3% in 1992 to 5.2% in 1996. Mortality rates declined in uninfected children from 5.4% in 1992 to 4.5% in 1996 (chi(2) trend 3.3; p = 0.06). CONCLUSION: The mortality rate of children has increased at Chris Hani Baragwanath Hospital as a result of HIV infection. Almost half the deaths were HIV related in 1996. HIV infection is threatening the advances that have been made on child survival in South Africa over the last few decades.

Age Distribution↗
PubMed2006

Utility of the MAYO End-Stage Liver Disease score, King's College Criteria, and a new in-hospital mortality score in the prognosis of in-hospital mortality in acute liver failure.

INTRODUCTION: Several prognostic scores attempt to aid in the selection of patients with acute liver failure (ALF) to be treated either medically or by liver transplantation; however, their lack of fulfillment does not predict spontaneous survival in ALF and refined prognostic criteria are needed to improve such selection. Our aim was to evaluate and compare a new ALF in-hospital mortality prediction score versus King's College Criteria (KCC) and model for End-Stage Disease (MELD) score. METHODS: First-time ALF-diagnosed individuals admitted to our institution (n = 58) were grouped according their final outcome as "alive" or "death," and those significantly different variables between groups entered into a logistic regression and lineal regression models. An ALF in-hospital mortality score (ALFIHMS) was produced and its sensitivity, specificity, and area under receiver operator characteristics were compared with those of KCC and MELD scores. RESULTS: Since no significant differences (P = .81) in mortality rates between fulminant and subfulminant hepatic failure were found, no further analysis according to ALF's classification was performed. After obtaining and comparing ALFIHMS with KCC and MELD, we found that ALFIHMS prediction accuracy is higher than that of KCC and MELD score and that an ALFIHMS cutoff point >15 points is associated with an in-hospital mortality probability >50%. CONCLUSIONS: ALFIHMS has higher prognostic accuracy than KCC and MELD scores in ALF.

Hospital Mortality↗
PubMed1991

The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.

The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).

Age Factors↗
PubMed1995

Hospital mortality after out-of-hospital cardiac arrest among patients found in ventricular fibrillation.

The aim of this study was to describe factors associated with in-hospital mortality among patients being hospitalised after out-of-hospital cardiac arrest and who were found in ventricular fibrillation. The study was set in the community of Göteborg, Sweden. The subjects consisted of all patients who were hospitalised alive after out-of-hospital cardiac arrest, being reached by our mobile coronary care unit and who were found in ventricular fibrillation, between 1981 and 1992. In all, 488 patients fulfilled the inclusion criteria of which 262 (54%) died during initial hospitalization. In a multivariate analysis including age, sex, history of cardiovascular disease, chronic medication prior to arrest and circumstances at the time of arrest, the following appeared as independent predictors of hospital mortality: (1) interval between collapse and first defibrillation (P < 0.001); (2) on chronic medication with diuretics (P < 0.01); (3) age (P < 0.01); (4) bystander initiated CPR (P < 0.05); and (5) a history of diabetes (P < 0.05). In a multivariate analysis considering various aspects of status on admission to hospital, the following were independently associated with death: (1) degree of consciousness (P < 0.001) and (2) systolic blood pressure (P < 0.05). In conclusion, among patients with out of hospital cardiac arrest found in ventricular fibrillation and being hospitalised alive, 54% died in hospital. The in-hospital mortality was related to patient characteristics before the cardiac arrest as well as to factors at the resuscitation itself.

Adolescent↗
PubMed2001

Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h.

UNLABELLED: In most databases used to build general severity scores the median duration of intensive care unit (ICU) stay is less than 3 days. Consequently, these scores are not the most appropriate tools for measuring prognosis in studies dealing with ICU patients hospitalized for more than 72 h. PURPOSE: To develop a new prognostic model based on a general severity score (SAPS II), an organ dysfunction score (LOD) and evolution of both scores during the first 3 days of ICU stay. DESIGN: Prospective multicenter study. SETTING: Twenty-eight intensive care units (ICUs) in France. PATIENTS: A training data-set was created with four ICUs during an 18-month period (893 patients). Seventy percent of the patients were medical (628) aged 66 years. The median SAPS II was 38. The ICU and hospital mortality rates were 22.7% and 30%, respectively. Forty-seven percent (420 patients) were transferred from hospital wards. In this population, the calibration (Hosmer-Lemeshow chi-square: 37.4, P = 0.001) and the discrimination [area under the ROC curves: 0.744 (95 % CI: 0.714-0.773)] of the original SAPS II were relatively poor. A validation data set was created with a random panel of 24 French ICUs during March 1999 (312 patients). MEASUREMENTS AND MAIN RESULTS: The LOD and SAPS II scores were calculated during the first (SAPS1, LOD1), second (SAPS2, LOD2), and third (SAPS3, LOD3) calendar days. The LOD and SAPS scores alterations were assigned the value "1" when scores increased with time and "0" otherwise. A multivariable logistic regression model was used to select variables measured during the first three calendar days, and independently associated with death. Selected variables were: SAPS II at admission [OR: 1.04 (95 % CI: 1.027-1.053) per point], LOD [OR: 1.16 (95 % CI: 1.085-1.253) per point], transfer from ward [OR: 1.74 (95 % CI: 1.25-2.42)], as well as SAPS3-SAPS2 alterations [OR: 1.516 (95 % CI: 1.04-2.22)], and LOD3-LOD2 alterations [OR: 2.00 (95 % CI: 1.29-3.11)]. The final model has good calibration and discrimination properties in the training data set [area under the ROC curve: 0.794 (95 % CI: 0.766-0.820), Hosmer-Lemeshow C statistic: 5.56, P = 0.7]. In the validation data set, the model maintained good accuracy [area under the ROC curve: 0.826 (95 % CI: 0.780-0.867), Hosmer-Lemeshow C statistic: 7.14, P = 0.5]. CONCLUSIONS: The new model using SAPS II and LOD and their evolution during the first calendar days has good discrimination and calibration properties. We propose its use for benchmarking and evaluating the over-risk of death associated with ICU-acquired nosocomial infections.

APACHE↗
PubMed1985

Predictors of hospital mortality after out-of-hospital cardiopulmonary resuscitation.

Hospital mortality was examined in all patients successfully resuscitated from a witnessed out-of-hospital cardiac arrest due to ventricular fibrillation over a 1-yr period. Variables independently predictive of hospital mortality were a history of congestive heart failure before cardiac arrest, the time between collapse and initiation of CPR, and the time between collapse and restoration of circulation. The latter time was not related to either patient age or clinical history. Thus, hospital mortality was predetermined by prehospital factors, some of which can be changed.

Age Factors↗
PubMed2000

Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals.

OBJECTIVE: To examine the relationship of in-hospital and 30-day mortality rates and the association between in-hospital mortality and hospital discharge practices. DATA SOURCES/STUDY SETTING: A secondary analysis of data for 13,834 patients with congestive heart failure who were admitted to 30 hospitals in northeast Ohio in 1992-1994. DESIGN: A retrospective cohort study was conducted. DATA COLLECTION: Demographic and clinical data were collected from patients' medical records and were used to develop multivariable models that estimated the risk of in-hospital and 30-day (post-admission) mortality. Standardized mortality ratios (SMRs) for in-hospital and 30-day mortality were determined by dividing observed death rates by predicted death rates. PRINCIPAL FINDINGS: In-hospital SMRs ranged from 0.54 to 1.42, and six hospitals were classified as statistical outliers (p <.05); 30-day SMRs ranged from 0.63 to 1.73, and seven hospitals were outliers. Although the correlation between in-hospital SMRs and 30-day SMRs was substantial (R = 0.78, p < .001), outlier status changed for seven of the 30 hospitals. Nonetheless, changes in outlier status reflected relatively small differences between in-hospital and 30-day SMRs. Rates of discharge to nursing homes or other inpatient facilities varied from 5.4 percent to 34.2 percent across hospitals. However, relationships between discharge rates to such facilities and in-hospital SMRs (R = 0.08; p = .65) and early post-discharge mortality rates (R = 0.23; p = .21) were not significant. CONCLUSIONS: SMRs based on in-hospital and 30-day mortality were relatively similar, although classification of hospitals as statistical outliers often differed. However, there was no evidence that in-hospital SMRs were biased by differences in post-discharge mortality or discharge practices.

Aged↗
PubMed1997

[Analysis of hospital mortality at a regional hospital].

The hospital mortality rate in our centre es 2.34% (264 deaths from a total of 11,336 discharges between 1991 and 1993). The most frequent causes are acute myocardial infarction and cerebrovascular accidents, followed in descending order by pneumonia, chronic bronchitis, congestive heart failure, upper GI haemorrhage, GI tumours, liver cirrhosis, lung tumours and arrhythmias. Our analysis reflects a mortality pattern of a rural population with an age pyramid in which 52% of the patients are older than 45 years. The pattern also reflects the little impact of accidents on our mortality. A 87% of the deaths were older than 65 years with a male to female ratio of 1, 6 and a Swaroop index of 93% and 94% for males and females respectively.

Adolescent↗
PubMed2001

Hospital volume and hospital mortality for esophagectomy.

BACKGROUND: Hospital mortality after esophagectomy has decreased from 29% to 7.5% over the last decades because of improved surgical techniques and better perioperative care. Suggestions have been made that a further decrease in hospital mortality may be achieved by centralization of esophagectomies in high volume centers. METHODS: The effect of hospital volume on hospital mortality after esophagectomy in the Netherlands was analyzed based on data from the Dutch National Medical Registry and the Dutch Network and National Database for Pathology over the period 1993-1998. RESULTS: Annually, approximately 310 (range, 264-321) esophagectomies are performed in the Netherlands. Fifty-two percent are performed in 43-55 low volume centers (1-10 resections a year). Six percent are performed in 1-3 medium volume centers (11-20 resections a year). The remainder (42%) is performed in two high volume centers (> 50 resections a year). Hospital mortality is 12.1%, 7.5% and 4.9% respectively (P < 0.001). The high volume centers seem to see slightly more advanced tumors than the low and medium volume centers. CONCLUSIONS: There is a significant (inverse) relation between hospital mortality and hospital volume for esophageal resection in the Netherlands. Although hospital mortality is not the only measure for quality of care, these data suggest a potential beneficial effect to centralization of esophagectomy in the Netherlands.

Aged↗
PubMed1995

In-hospital mortality after out-of-hospital cardiac arrest.

In-hospital management of out-of-hospital cardiac arrest is complicated by uncertainty about prognosis and the need to identify markers of adverse outcome in individuals surviving initial resuscitation. We sought to identify factors that predict in-hospital death among patients who initially survive out-of-hospital cardiac arrest. We investigated 346 consecutive cases of out-of-hospital cardiac arrest received by a single centre in Edinburgh, UK (270 cases examined retrospectively, 76 prospectively). Of the retrospective cohort, 246 cases were thought to be of cardiac origin. There were associations between in-hospital mortality and pre-arrest variables, resuscitation variables, and factors measured during admission. Crew-witnessed arrests were associated with low mortality; arrest rhythm (p < 0.001), resuscitation by a health professional (p < 0.05), conscious level on admission (p < 0.001), and requirement for ventilation (p < 0.05) independently predicted in-hospital mortality. A weighted prognostic scoring system based on three of these variables accurately predicted the likelihood of in-hospital death in the prospective test group. Further assessment of conscious level during admission with the Glasgow coma score predicted mortality rates in the study population, but coma did not predict a hopeless prognosis in individual cases unless it persisted for 72 h or more. Accurate prognostic assessment of out-of-hospital cardiac arrest survivors can be made from information available on admission. Of factors that independently predicted outcome, the skill of the resuscitator is most readily modified. This suggests that public training in resuscitation may reduce mortality rates.

Adult↗
PubMed2003

Hospital discharge to other healthcare facilities: impact on in-hospital mortality.

BACKGROUND: In-hospital mortality is frequently used as an outcomes measure for surgical procedures. Recently, hospitals have developed subacute care facilities to allow earlier discharge. Outcomes of patients discharged (transferred) to these units or to other similar facilities may not be captured in reports of in-hospital mortality. STUDY DESIGN: The California Office of Statewide Health Planning and Development (OSHPD) patient discharge abstract database was examined to determine the rates of discharge to other facilities (transfer) and the number of in-hospital deaths occurring during the index hospitalization and after transfer in patients undergoing cardiac surgery procedures. Data were collected for 1997, 1998, and 1999 for coronary artery bypass grafting (CABG-only, n = 82,897), CABG plus additional procedures (CABG-plus, n = 11,869), and valve repair or replacement (Valve-only, n = 14,872). In-hospital mortality and transfer rates (same-day discharge and readmission to another facility) were determined for all hospitals through the index hospitalization and subsequent transfers. RESULTS: Aggregated 3-year in-hospital mortality rates for the index hospitalization were 2.98% for CABG-only, 9.25% for CABG-plus, and 4.85% in Valve-only groups. Transfer rates were 12.41%, 23.16%, and 13.43%, respectively. The percentages of all in-hospital deaths occurring after transfer from the index hospital were 13.5% (385 of 2,857) in CABG-only, 13.3% (168 of 1,266) in CABG-plus, and 11.0% (89 of 811) in Valve-only patients. When corrected for these additional deaths, the actual in-hospital mortality rate was 3.45% for CABG-only, 10.67% for CABG-plus, and 5.45% for Valve-only procedures. CONCLUSIONS: Transfer to another healthcare facility rather than discharge home is a common practice after cardiac surgery. A substantial percentage of in-hospital deaths occurs after discharge from the primary institution.

Bias↗
PubMed1991

Modeling organizational determinants of hospital mortality.

This study examines hospital characteristics that affect the differential in hospital mortality. Death rates for 1984 Medicare inpatients in acute care hospitals, released by the Health Care Financing Administration in 1986, were analyzed. A confirmatory statistical approach to organizational determinants of hospital mortality was formulated and validated through an empirical examination of 239 hospitals. The findings suggest that the effect of hospital size and specialization on mortality was a spurious one when the effects of other variables were simultaneously controlled. A positive association existed between service intensity and hospital mortality: the more hospital services consumed, the higher the mortality rate. Community attributes accounted for more variance in hospital mortality rates than did organizational attributes. The organizational and community factors studied explained 27 percent of the total variance in hospital mortality.

Catchment Area, Health↗
PubMed2004

[Use of hospital mortality rates following myocardial revascularization surgery to monitor hospital care].

Brazilian hospitals performing coronary artery by-pass graft surgery (CABG) from 1996 to 1998 and covered by the Ministry of Health were ranked according to their risk-adjusted hospital mortality rates. Seventy-six hospitals that performed more than 150 CABGs (total of 38,962 surgical interventions) were classified as low or high outliers according to the ratio between observed and expected hospital mortality rates. Overall hospital mortality rate was 7.20%. The rate was 3.48% among patients treated in the low outliers and 13.96% among the high outliers. The methodology was useful for discriminating Brazilian hospitals according to their post-CABG mortality rates and may be a useful tool for identifying hospitals with possible quality-of-care problems.

Adult↗
PubMedDate not supplied

Frequency with which surgeons undertake pancreaticoduodenectomy determines length of stay, hospital charges, and in-hospital mortality.

Others have suggested that in certain technically challenging operations, outcome and experience are related. Because pancreaticoduodenectomy is a technically complex procedure, this study was undertaken to evaluate mortality, length of hospital stay, and hospital charges when compared to volume of experience. The database of the State of Florida Agency for Health Care Administration was queried for pancreaticoduodenectomies undertaken during a recent 33-month period. Length of stay, hospital charges, and in-hospital mortality were stratified by the frequency of pancreaticoduodenectomy. A total of 282 surgeons performed 698 pancreaticoduodenectomies over 33 months. Eighty-nine percent of surgeons performed one pancreaticoduodenectomy per year or less and accounted for 52% of the procedures. Overall mortality rate was 5.1%. Average hospital charges were &dollar;72,171.64. The more frequently pancreaticoduodenectomy was undertaken, the shorter the hospital stay (P = 0.025, regression analysis) and the lower the hospital charges (P = 0.008, regression analysis) and in-hospital mortality (P = 0.036, log likelihood ratio test). Surgeons who undertake pancreaticoduodenectomy more frequently have patients with shorter hospital stays, lower hospital charges, and lower in-hospital mortality rates, independent of hospital volume. Variations exist among surgeons and among different areas of the state. Data regarding cost and mortality are available for use in programs of cost and quality improvement.

Academic Medical Centers↗