The APACHE III Prognostic System

Knaus, William A.; Wagner, Douglas P.; Draper, Elizabeth A.; Zimmerman, Jack E.; Bergner, Marilyn; Bastos, Paulo G.; Sirio, Carl A.; Murphy, Douglas B.; Lotring, Ted; Damiano, Anne M.; Harrell, Frank E.

doi:10.1378/chest.100.6.1619

Cited by 3,196 publications

(605 citation statements)

References 40 publications

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“…A standardized case report form was used to extract the following data from the patients' hospital records: age, sex, past medical and surgical history, Candida species type and date of culture, presence of a CVC and/or indwelling urinary catheter, total parenteral nutrition (TPN) administration, immunosuppressive therapy (antineoplastic agents and corticosteroids) within 30 days before infection, degree and duration of neutropenia, Acute Physiology and Chronic Health Evaluation III (APACHE III) score (16), type of hospital admission (medical or surgical), location of patient at time of blood culture (ICU or non-ICU), and duration of hospitalization. The type, dose, and duration of antifungal therapy were also identified.…”

Section: Methodsmentioning

confidence: 99%

Biofilm Production by Candida Species and Inadequate Antifungal Therapy as Predictors of Mortality for Patients with Candidemia

et al. 2007

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1.09 to 5.10; P ‫؍‬ 0.03), infection with overall biofilm-forming Candida species (OR, 2.33; 95% CI, 1.26 to 4.30; P ‫؍‬ 0.007), and Acute Physiology and Chronic Health Evaluation III scores (OR, 1.03; 95% CI, 1.01 to 1.15; P < 0.001) as independent predictors of mortality. Notably, if mortality was analyzed according to the different biofilm-forming Candida species studied, only infections caused by C. albicans (P < 0.001) and C. parapsilosis (P ‫؍‬ 0.003) correlated with increased mortality. Together with well-established factors, Candida biofilm production was therefore shown to be associated with greater mortality of patients with candidemia, probably by preventing complete organism eradication from the blood.

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Section: Methodsmentioning

confidence: 99%

Biofilm Production by Candida Species and Inadequate Antifungal Therapy as Predictors of Mortality for Patients with Candidemia

et al. 2007

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“…Failho et al compared the results they got from their set of 6 physiological variables (determined by SFS) in conjunction with TS to that of the sets determined by APACHE II and APACHE III [42] scores (also in conjunction with TS). APACHE III is another SoDCS, and was created with the goal of improving some of the problems in APACHE III as discussed in [42] The research objective of Mathias et al [15] is along a slightly different line where instead of trying to predict ICU readmission they look to predict a 5 year mortality rate through the construction of an Ensemble Index (EI).…”

Section: Prediction Of Icu Readmission and Mortality Ratementioning

confidence: 99%

“…APACHE III is another SoDCS, and was created with the goal of improving some of the problems in APACHE III as discussed in [42] The research objective of Mathias et al [15] is along a slightly different line where instead of trying to predict ICU readmission they look to predict a 5 year mortality rate through the construction of an Ensemble Index (EI). They used a group of 7463 patients taken from an Electronic Health Record (EHR) along with 980 attributes for each patient.…”

Section: Prediction Of Icu Readmission and Mortality Ratementioning

confidence: 99%

A review of data mining using big data in health informatics

Herland

Khoshgoftaar

Wald

2014

Journal of Big Data

257

135

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The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this information can improve the quality of healthcare offered to patients. However, there are a number of issues that arise when dealing with these vast quantities of data, especially how to analyze this data in a reliable manner. The basic goal of Health Informatics is to take in real world medical data from all levels of human existence to help advance our understanding of medicine and medical practice. This paper will present recent research using Big Data tools and approaches for the analysis of Health Informatics data gathered at multiple levels, including the molecular, tissue, patient, and population levels. In addition to gathering data at multiple levels, multiple levels of questions are addressed: human-scale biology, clinical-scale, and epidemic-scale. We will also analyze and examine possible future work for each of these areas, as well as how combining data from each level may provide the most promising approach to gain the most knowledge in Health Informatics.

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“…The retrospective National Institutes of Health Stroke Scale (rNIHSS) [15] was used as an adjustment for stroke severity and was categorized as mild (NIHSS 2), moderate (NIHSS 3-9), and severe (NIHSS 10). The admission modified Acute Physiology and Chronic Health Evaluation III was used to adjust for the overall patient disease severity [16]. Admission code status was classified as full code (i.e., do not resuscitate or do not intubate) versus other.…”

Section: Patient and Facility Characteristicsmentioning

confidence: 99%

Postdischarge quality of care: Do age disparities exist among Department of Veterans Affairs ischemic stroke patients?

Chumbler

Jia

Phipps

et al. 2013

JRRD

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Abstract-This study examined whether age disparities existed across postdischarge quality indicators (QIs) for veterans with ischemic stroke who received care at Department of Veterans Affairs medical centers (VAMCs). This retrospective cohort included a national sample of 3,196 veterans who were diagnosed with ischemic stroke and received acute and postdischarge stroke care at 127 VAMCs in fiscal year 2007 (10/1/06 through 9/30/07). Data included an assessment of postdischarge stroke QIs in the outpatient setting during the 6 mo postdischarge. The QIs included measurement of and goal achievement for (1) blood pressure, (2) serum international normalized ratio (INR) for all patients discharged on warfarin, (3) cholesterol (low-density lipoprotein [LDL]) levels, (4) serum glycosylated hemoglobin, and (5) depression treatment. The mean age for the 3,196 veterans included in this study was 67.2 +/-11.3 yr. Before risk adjustment, there were age differences in (1) depression screening/treatment, (2) blood pressure goals, and (3) LDL levels. After we adjusted for patient sociodemographic, clinical, and facility-level characteristics by using hierarchical linear mixed modeling, none of these differences remained significant but INR goals for patients discharged on warfarin differed significantly by age. After we adjusted for patient and facility characteristics, fewer age differences were found in the postdischarge stroke QIs. Clinical trial registration was not required.

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The APACHE III Prognostic System

Cited by 3,196 publications

References 40 publications

Biofilm Production by Candida Species and Inadequate Antifungal Therapy as Predictors of Mortality for Patients with Candidemia

Biofilm Production by Candida Species and Inadequate Antifungal Therapy as Predictors of Mortality for Patients with Candidemia

A review of data mining using big data in health informatics

Postdischarge quality of care: Do age disparities exist among Department of Veterans Affairs ischemic stroke patients?

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