Validation of Diagnosis Codes to Identify Infection-Related Acute Care Events in Patients With Glomerular Disease.
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ABSTRACT:
SUBMITTER: Glenn DA
PROVIDER: S-EPMC8640562 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivesInfections contribute to patient morbidity and mortality in glomerular disease. We sought to describe the incidence of, and identify risk factors for, infection-related acute care events among Cure Glomerulonephropathy Network (CureGN) study participants.Design, setting, participants, & measurementsCureGN is a prospective, multicenter, cohort study of children and adults with biopsy sample-proven minimal change disease, FSGS, membranous nephropathy, or IgA nephropathy/vasculitis. Risk factors for time to first infection-related acute care events (hospitalization or emergency department visit) were identified using multivariable Cox proportional hazards regression.ResultsOf 1741 participants (43% female, 41% <18 years, 68% White), 163 (9%) experienced infection-related acute care events over a median follow-up of 17 months (interquartile range, 9-26 months). Unadjusted incidence rates of infection-related acute care events were 13.2 and 6.2 events per 100 person-years among pediatric and adult participants, respectively. Among participants with versus without corticosteroid exposure at enrollment, unadjusted incidence rates were 50.6 and 28.6 per 100 person-years, respectively, during the first year of follow-up (adjusted hazard ratio for time to first infection, 1.31; 95% CI, 0.89 to 1.93), and 4.1 and 1.1 per 100 person-years, respectively, after 1 year of follow-up (hazard ratio, 2.99; 95% CI, 1.54 to 5.79). Hypoalbuminemia combined with nephrotic-range proteinuria (serum albumin ≤2.5 g/dl and urinary protein-creatinine ratio >3.5 mg/mg), compared with serum albumin >2.5 g/dl and urinary protein-creatinine ratio ≤3.5 mg/mg, was associated with higher risk of time to first infection (adjusted hazard ratio, 2.49; 95% CI, 1.51 to 4.12).ConclusionsAmong CureGN participants, infection-related acute care events were common and associated with younger age, corticosteroid exposure, and hypoalbuminemia with proteinuria.
| S-EPMC7769021 | biostudies-literature
Project description:ObjectivesHospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults.Setting and participantsWe identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008-2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.DesignMedical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume.MeasuresTwo trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.ResultsThe PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).ConclusionsDischarge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.
| S-EPMC6009457 | biostudies-literature
Project description:BackgroundThe use of real-world data to generate evidence requires careful assessment and validation of critical variables before drawing clinical conclusions. Prospective clinical trial data suggest that anatomic origin of colon cancer impacts prognosis and treatment effectiveness. As an initial step in validating this observation in routine clinical settings, we explored the feasibility and accuracy of obtaining information on tumor sidedness from electronic health records (EHR) billing codes.MethodsNine thousand four hundred three patients with metastatic colorectal cancer (mCRC) were selected from the Flatiron Health database, which is derived from de-identified EHR data. This study included a random sample of 200 mCRC patients. Tumor site data derived from International Classification of Diseases (ICD) codes were compared with data abstracted from unstructured documents in the EHR (e.g. surgical and pathology notes). Concordance was determined via observed agreement and Cohen's kappa coefficient (?). Accuracy of ICD codes for each tumor site (left, right, transverse) was determined by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and corresponding 95% confidence intervals, using abstracted data as the gold standard.ResultsStudy patients had similar characteristics and side of colon distribution compared with the full mCRC dataset. The observed agreement between the ICD codes and abstracted data for tumor site for all sampled patients was 0.58 (??=?0.41). When restricting to the 62% of patients with a side-specific ICD code, the observed agreement was 0.84 (??=?0.79). The specificity (92-98%) of structured data for tumor location was high, with lower sensitivity (49-63%), PPV (64-92%) and NPV (72-97%). Demographic and clinical characteristics were similar between patients with specific and non-specific side of colon ICD codes.ConclusionsICD codes are a highly reliable indicator of tumor location when the specific location code is entered in the EHR. However, non-specific side of colon ICD codes are present for a sizable minority of patients, and structured data alone may not be adequate to support testing of some research hypotheses. Careful assessment of key variables is required before determining the need for clinical abstraction to supplement structured data in generating real-world evidence from EHRs.
| S-EPMC6700780 | biostudies-literature
Project description:ObjectivesValidation studies in oncology are limited in Japan. This study was conducted to evaluate the accuracy of diagnosis and adverse event (AE) definitions for specific cancers in a Japanese health administrative real-world database (RWD).Design and settingRetrospective observational validation study to assess the diagnostic accuracy of electronic medical records (EMRs) and claim coding regarding oncology diagnosis and AEs based on medical record review in the RWD. The sensitivity and positive predictive value (PPV) with 95% CIs were calculated.ParticipantsThe validation cohort included patients with lung (n=2257), breast (n=1121), colorectal (n=1773), ovarian (n=216) and bladder (n=575) cancer who visited the hospital between January 2014 and December 2018, and those with prostate cancer (n=3491) visiting between January 2009 and December 2018, who were identified using EMRs.OutcomesKey outcomes included primary diagnosis, deaths and AEs.ResultsFor primary diagnosis, sensitivity and PPV for the respective cancers were as follows: lung, 100.0% (96.6 to 100.0) and 81.0% (74.9 to 86.2); breast, 100.0% (96.3 to 100.0) and 74.0% (67.3 to 79.9); colorectal, 100.0% (96.6 to 100.0) and 80.5% (74.3 to 85.8); ovarian, 89.8% (77.8 to 96.6) and 75.9% (62.8 to 86.1); bladder, 78.6% (63.2 to 89.7) and 67.3% (52.5 to 0.1); prostate, 100.0% (93.2 to 100.0) and 79.0% (69.7 to 86.5). Sensitivity and PPV for death were as follows: lung, 97.0% (84.2 to 99.9) and 100.0% (84.2 to 100.0); breast, 100.0% (1.3 to 100.0) and 100.0% (1.3 to 100.0); colorectal, 100.0% (28.4 to 100.0) and 100.0% (28.4 to 100.0); ovarian, 100.0% (35.9 to 100.0) and 100.0% (35.9 to 100.0); bladder, 100.0% (9.4-100.0) and 100.0% (9.4 to 100.0); prostate, 75.0% (19.4 to 99.4) and 100.0% (19.4 to 100.0). Overall, PPV tended to be low, with the definition based on International Classification of Diseases, 10th revision alone for AEs.ConclusionDiagnostic accuracy was not so high, and therefore needs to be further investigated.Trial registration numberUniversity Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000039345).
| S-EPMC9280899 | biostudies-literature
Project description:Identification of acute liver failure (ALF) is important for post-marketing surveillance of medications, but the validity of using ICD-9 diagnoses and laboratory data to identify these events within electronic health records is unknown. We examined positive predictive values (PPVs) of hospital ICD-9 diagnoses and laboratory tests of liver dysfunction for identifying ALF within a large, community-based integrated care organization.We identified Kaiser Permanente Northern California health plan members (2004-2010) with a hospital diagnosis suggesting ALF (ICD-9 570, 572.2, 572.4, 572.8, 573.3, 573.8, or V42.7) plus an inpatient international normalized ratio ?1.5 (off warfarin) and total bilirubin ?5.0?mg/dL. Hospital records were reviewed by hepatologists to adjudicate ALF events. PPVs for confirmed outcomes were determined for individual ICD-9 diagnoses, diagnoses plus prescriptions for hepatic encephalopathy treatment, and combinations of diagnoses in the setting of coagulopathy and hyperbilirubinemia.Among 669 members with no pre-existing liver disease, chart review confirmed ALF in 62 (9%). Despite the presence of co-existing coagulopathy and hyperbilirubinemia, individual ICD-9 diagnoses had low PPVs (range, 5-15%); requiring prescriptions for encephalopathy treatment did not increase PPVs of these diagnoses (range, 2-23%). Hospital diagnoses of other liver disorders (ICD-9 573.8) plus hepatic coma (ICD-9 572.2) had high PPV (67%; 95%CI, 9-99%) but only identified two (3%) ALF events.Algorithms comprising relevant hospital diagnoses, laboratory evidence of liver dysfunction, and prescriptions for hepatic encephalopathy treatment had low PPVs for confirmed ALF events. Studies of ALF will need to rely on medical records to confirm this outcome.
| S-EPMC4509617 | biostudies-literature
Project description:BackgroundHealth care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD).MethodsWe searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality.ResultsWe identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%.ConclusionsDiagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses.
| S-EPMC8134528 | biostudies-literature
Project description:To validate, using physician review of abstracted medical chart data as a gold standard, a claims-based algorithm developed to identify gastrointestinal (GI) perforation cases among rheumatoid arthritis (RA) patients.Patients with established RA, aged 18?years or older with hospital admissions between January 2004 and September 2009, were selected from a large US-hospital-based database. An algorithm with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes for GI perforation and combinations of GI-related diagnosis codes and Current Procedural Terminology (CPT-4) procedure codes for relevant GI surgeries was used to identify potential GI perforation cases. Two senior experienced specialist physicians independently reviewed abstracted chart data and classified cases as confirmed or unconfirmed GI perforations. Positive predictive values (PPVs) to identify confirmed GI perforation were calculated and stratified by upper versus lower GI tract.Overall, 86 of 92 GI perforation cases were confirmed, yielding an overall PPV of 94% (95%confidence interval [CI]?=?86%-98%). PPV was 100% (95%CI?=?100%-100%) for upper GI perforation (esophagus, stomach) and 91% (95%CI?=?90%-97%) for lower GI perforation (small intestine, PPV?=?100%; large intestine, PPV?=?94%; unspecified lower GI, PPV?=?89%).This algorithm, consisting of a combination of ICD-9-CM diagnosis and CPT-4 codes, could be used in future safety studies to evaluate GI perforation risk factors in RA patients.
| S-EPMC3227025 | biostudies-literature
Project description:BACKGROUND Few studies have validated ICD-9-CM diagnosis codes for surgical site infection (SSI), and none have validated coding for noninfectious wound complications after mastectomy. OBJECTIVES To determine the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in health insurer claims data to identify SSI and noninfectious wound complications, including hematoma, seroma, fat and tissue necrosis, and dehiscence, after mastectomy. METHODS We reviewed medical records for 275 randomly selected women who were coded in the claims data for mastectomy with or without immediate breast reconstruction and had an ICD-9-CM diagnosis code for a wound complication within 180 days after surgery. We calculated the positive predictive value (PPV) to evaluate the accuracy of diagnosis codes in identifying specific wound complications and the PPV to determine the accuracy of coding for the breast surgical procedure. RESULTS The PPV for SSI was 57.5%, or 68.9% if cellulitis-alone was considered an SSI, while the PPV for cellulitis was 82.2%. The PPVs of individual noninfectious wound complications ranged from 47.8% for fat necrosis to 94.9% for seroma and 96.6% for hematoma. The PPVs for mastectomy, implant, and autologous flap reconstruction were uniformly high (97.5%-99.2%). CONCLUSIONS Our results suggest that claims data can be used to compare rates of infectious and noninfectious wound complications after mastectomy across facilities, even though PPVs vary by specific type of postoperative complication. The accuracy of coding was highest for cellulitis, hematoma, and seroma, and a composite group of noninfectious complications (fat necrosis, tissue necrosis, or dehiscence). Infect Control Hosp Epidemiol 2017;38:334-339.
| S-EPMC5490368 | biostudies-literature
Project description:Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development.Determine the most accurate International Classification of Diseases, Ninth Revision (ICD-9) diagnosis coding strategies for identification of febrile infants.Retrospective cross-sectional study.Eight emergency departments in the Pediatric Health Information System.Infants aged <90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from 1 of 4 ICD-9 diagnosis code groups: (1) discharge diagnosis of fever, (2) admission diagnosis of fever without discharge diagnosis of fever, (3) discharge diagnosis of serious infection without diagnosis of fever, and (4) no diagnosis of fever or serious infection.The ICD-9 diagnosis code groups were compared in 4 case-identification algorithms to a reference standard of fever ?100.4°F documented in the medical record.Algorithm predictive accuracy was measured using sensitivity, specificity, and negative and positive predictive values.Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3).A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though underclassification of patients is a potential limitation.
| S-EPMC4715646 | biostudies-literature
Project description:This clinical trial studies if enhanced outpatient symptom management with telemedicine and remote monitoring can help reduce acute care visit due to chemotherapy-related adverse events. Receiving telemedicine and remote monitoring may help patients have better outcomes (such as fewer avoidable emergency room visits and hospitalizations, better quality of life, fewer symptoms, and fewer treatment delays) than patients who receive usual care.
| 2389176 | ecrin-mdr-crc