Epidemiology of Kawasaki Disease in Canada 2004 to 2014: Comparison of Surveillance Using Administrative Data vs Periodic Medical Record Review

2018 ◽  
Vol 34 (3) ◽  
pp. 303-309 ◽  
Author(s):  
Cedric Manlhiot ◽  
Sunita O'Shea ◽  
Bailey Bernknopf ◽  
Michael LaBelle ◽  
Nita Chahal ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Medical Record ◽  
Administrative Data ◽  
Medical Record Review ◽  
Record Review

scholarly journals Administrative data is as good as medical chart review for comorbidity ascertainment in patients with infections in Singapore

2016 ◽  
Vol 144 (9) ◽  
pp. 1999-2005 ◽  
Author(s):  
J. HWANG ◽  
A. CHOW ◽  
D. C. LYE ◽  
C. S. WONG
Keyword(s):  
Infectious Disease ◽  
Hospital Mortality ◽  
Medical Record ◽  
Administrative Data ◽  
Illness Severity ◽  
Medical Record Review ◽  
Disease Research ◽  
Infectious Disease Research ◽  
Infection Source ◽  
Record Review

SUMMARYThe Charlson comorbidity index (CCI) is widely used for control of confounding from comorbidities in epidemiological studies. International Classification of Diseases (ICD)-coded diagnoses from administrative hospital databases is potentially an efficient way of deriving CCI. However, no studies have evaluated its validity in infectious disease research. We aim to compare CCI derived from administrative data and medical record review in predicting mortality in patients with infections. We conducted a cross-sectional study on 199 inpatients. Correlation analyses were used to compare comorbidity scores from ICD-coded administrative databases and medical record review. Multivariable regression models were constructed and compared for discriminatory power for 30-day in-hospital mortality. Overall agreement was fair [weighted kappa 0·33, 95% confidence interval (CI) 0·23–0·43]. Kappa coefficient ranged from 0·17 (95% CI 0·01–0·36) for myocardial infarction to 0·85 (95% CI 0·59–1·00) for connective tissue disease. Administrative data-derived CCI was predictive of CCI ⩾5 from medical record review, controlling for age, gender, resident status, ward class, clinical speciality, illness severity, and infection source (C = 0·773). Using the multivariable model comprising age, gender, resident status, ward class, clinical speciality, illness severity, and infection source to predict 30-day in-hospital mortality, administrative data-derived CCI (C = 0·729) provided a similar C statistic as medical record review (C = 0·717, P = 0·8548). In conclusion, administrative data-derived CCI can be used for assessing comorbidities and confounding control in infectious disease research.

Validating an algorithm for multiple myeloma based on administrative data using a SEER tumor registry and medical record review

2019 ◽  
Vol 28 (2) ◽  
pp. 256-263 ◽  
Author(s):  
Nancy A. Brandenburg ◽  
Syd Phillips ◽  
Karen E. Wells ◽  
Kimberley J. Woodcroft ◽  
Kandace L. Amend ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Medical Record ◽  
Administrative Data ◽  
Medical Record Review ◽  
Tumor Registry ◽  
Record Review

scholarly journals Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Catherine L. Satterwhite ◽  
Onchee Yu ◽  
Marsha A. Raebel ◽  
Stuart Berman ◽  
Penelope P. Howards ◽  
...  
Keyword(s):  
Pelvic Inflammatory Disease ◽  
Medical Record ◽  
Administrative Data ◽  
Inflammatory Disease ◽  
Regression Tree ◽  
Case Finding ◽  
Medical Record Review ◽  
Classification And Regression Tree ◽  
Case Identification ◽  
Record Review

ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15–44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15–25 years as predictors. Algorithm sensitivity (GH=96.4%;KPCO=90.3%) and PPV (GH=86.9%;KPCO=84.5%) were high, but specificity was poor (GH=45.9%;KPCO=37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.

Comparison of the Use of Administrative Data and an Active System for Surveillance of Invasive Aspergillosis

2008 ◽  
Vol 29 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Douglas C. Chang ◽  
Lauren A. Burwell ◽  
G. Marshall Lyon ◽  
Peter G. Pappas ◽  
Tom M. Chiller ◽  
...  
Keyword(s):  
Invasive Aspergillosis ◽  
Quality Assessment ◽  
Medical Record ◽  
Administrative Data ◽  
Surveillance System ◽  
Medical Record Review ◽  
Solid Organ ◽  
Cell Transplant ◽  
Transplant Recipients ◽  
Record Review

Background.Administrative data, such as International Classification of Diseases, Ninth Revision (ICD-9) codes, are readily available and are an attractive option for surveillance and quality assessment within a single institution or for interinstitutional comparisons. To understand the usefulness of administrative data for the surveillance of invasive aspergillosis, we compared information obtained from a system based on ICD-9 codes with information obtained from an active, prospective surveillance system, which used more extensive case-finding methods (Transplant Associated Infection Surveillance Network).Methods.Patients with suspected inyasive aspergillosis were identified by aspergillosis-related ICD-9 codes assigned to hematopoietic stem cell transplant recipients and solid organ transplant recipients at a single hospital from April 1, 2001, through January 31, 2005. Suspected cases were classified as proven or probable invasive aspergillosis by medical record review using standard definitions. We calculated the sensitivity and positive predictive value (PPV) of identifying invasive aspergillosis by individual ICD-9 codes and by combinations of codes.Results.The sensitivity of code 117.3 was modest (63% [95% confidence interval {CI}, 38%-84%]), as was the PPV (71% [95% CI, 44%-90%]); the sensitivity of code 117.9 was poor (32% [95% CI, 13%-57%]), as was the PPV (15% [95% CI, 6%-31%]). The sensitivity of codes 117.3 and 117.9 combined was 84% (95% CI, 60%-97%); the PPV of the combined codes was 30% (95% CI, 18%-44%). Overall, ICD-9 codes triggered a review of medical records for 64 medical patients, only 16 (25%) of whom had proven or probable invasive aspergillosis.Conclusions.A surveillance system that involved multiple ICD-9 codes was sufficiently sensitive to identify most cases of invasive aspergillosis; however, the poor PPV of ICD-9 codes means that this approach is not adequate as the sole tool used to classify cases. Screening ICD-9 codes to trigger a medical record review might be a useful method of surveillance for invasive aspergillosis and quality assessment, although more investigation is needed.

scholarly journals Validation of ICD-10-CM codes for injuries complicating pregnancy, childbirth and the puerperium: a medical record review

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i9-i12
Author(s):  
Anna Hansen ◽  
Dana Quesinberry ◽  
Peter Akpunonu ◽  
Julia Martin ◽  
Svetla Slavova
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
International Classification Of Diseases ◽  
Medical Record Review ◽  
Full Sample ◽  
Emergency Department Data ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Record Review

IntroductionThe purpose of this study was to estimate the positive predictive value (PPV) of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for injury, poisoning, physical or sexual assault complicating pregnancy, childbirth and the puerperium (PCP) to capture injury encounters within both hospital and emergency department claims data.MethodsA medical record review was conducted on a sample (n=157) of inpatient and emergency department claims from one Kentucky healthcare system from 2015 to 2017, with any diagnosis in the ICD-10-CM range O9A.2-O9A.4. Study clinicians reviewed medical records for the sampled cases and used an abstraction form to collect information on documented presence of injury and PCP complications. The study estimated the PPVs and the 95% CIs of O9A.2-O9A.4 codes for (1) capturing injuries and (2) capturing injuries complicating PCP.ResultsThe estimated PPV for the codes O9A.2-O9A.4 to identify injury in the full sample was 79.6% (95% CI 73.3% to 85.9%) and the PPV for capturing injuries complicating PCP was 72.0% (95% CI 65.0% to 79.0%). The estimated PPV for an inpatient principal diagnosis O9A.2-O9A.4 to capture injuries was 90.7% (95% CI 82.0% to 99.4%) and the PPV for capturing injuries complicating PCP was 88.4% (95% CI 78.4% to 98.4%). The estimated PPV for any mention of O9A.2-O9A.4 in emergency department data to capture injuries was 95.2% (95% CI 90.6% to 99.9%) and the PPV for capturing injuries complicating PCP was 81.0% (95% CI 72.4% to 89.5%).DiscussionThe O9A.2-O9A.4 codes captured high percentage true injury cases among pregnant and puerperal women.

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