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Related Concept Videos

Data Collection I01:30

Data Collection I

8.2K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Comparison of comorbidity collection methods.

Dorina Kallogjeri1, Sheila M Gaynor1, Marilyn L Piccirillo1

  • 1Clinical Outcomes Research Office, Department of Otolaryngology-Head and Neck Surgery, Washington University in St Louis, St Louis, MO.

Journal of the American College of Surgeons
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Summary
This summary is machine-generated.

Comparing the Adult Comorbidity Evaluation-27 index (ACE-27) and Charlson Comorbidity Index (CCI) in cancer patients revealed differences in identifying comorbidities. Both indices proved significant for predicting patient survival, offering unique prognostic insights.

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Area of Science:

  • Oncology
  • Health Services Research
  • Biostatistics

Background:

  • Established comorbidity indices exist to assess their impact on cancer patient survival.
  • Comparison of chart-based Adult Comorbidity Evaluation-27 index (ACE-27) and claims-based Charlson Comorbidity Index (CCI) is crucial for accurate comorbidity assessment.
  • Understanding comorbidity burden is vital for prognostic accuracy in cancer care.

Purpose of the Study:

  • To compare the Adult Comorbidity Evaluation-27 index (ACE-27) and Charlson Comorbidity Index (CCI) in identifying comorbidities in cancer patients.
  • To evaluate the prognostic abilities of chart-based (ACE-27) versus claims-based (CCI) comorbidity assessment methods.
  • To determine which comorbidity index provides superior prognostic information for cancer patient survival.

Main Methods:

  • A prospective cohort study involved 6,138 newly diagnosed cancer patients across 12 institutions.
  • Trained registrars collected comorbidities using the ACE-27 method from patient charts.
  • ACE-27 assessments were compared against comorbidities identified via ICD coding from hospital discharge face sheets.
  • Prognostic performance was evaluated using 24-month follow-up data.

Main Results:

  • ACE-27 identified comorbidity distributions as: none (24%), mild (39%), moderate (22%), and severe (15%).
  • CCI identified patients with scores of 0 (69%), 1 (22%), 2 (6%), or 3+ (3%). Notably, 9% of patients with CCI score 0 had severe comorbidities by ACE-27.
  • Both ACE-27 and CCI scores were significantly associated with increased mortality risk.
  • A multivariable Cox model incorporating both indices demonstrated the best performance (Nagelkerke's R(2) = 0.37) and discrimination (C index = 0.827).

Conclusions:

  • Chart-based (ACE-27) and claims-based (CCI) methods identified different numbers, types, and severities of comorbidities in newly diagnosed cancer patients.
  • Both comorbidity indices demonstrated prognostic significance.
  • Each index provided unique and valuable prognostic information, suggesting complementary roles in cancer patient assessment.