To examine the 1-month prevalence of generalized anxiety disorder (GAD) according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), Diagnostic and Statistical Manual of Mental, Fifth Edition (DSM-V), and International Classification of Diseases, Tenth Revision (ICD-10), and the overlap between these criteria, in a population sample of 75-year-olds. We also aimed to examine comorbidity between GAD and other psychiatric diagnoses, such as depression.
During 2005-2006, a comprehensive semistructured psychiatric interview was conducted by trained nurses in a representative population sample of 75-year-olds without dementia in Gothenburg, Sweden (N = 777; 299 men and 478 women). All psychiatric diagnoses were made according to DSM-IV. GAD was also diagnosed according to ICD-10 and DSM-V.
The 1-month prevalence of GAD was 4.1% (N = 32) according to DSM-IV, 4.5% (N = 35) according to DSM-V, and 3.7% (N = 29) according to ICD-10. Only 46.9% of those with DSM-IV GAD fulfilled ICD-10 criteria, and only 51.7% and 44.8% of those with ICD-10 GAD fulfilled DSM-IV/V criteria. Instead, 84.4% and 74.3% of those with DSM-IV/V GAD and 89.7% of those with ICD-10 GAD had depression. Also other psychiatric diagnoses were common in those with ICD-10 and DSM-IV GAD. Only a small minority with GAD, irrespective of criteria, had no other comorbid psychiatric disorder. ICD-10 GAD was related to an increased mortality rate.
While GAD was common in 75-year-olds, DSM-IV/V and ICD-10 captured different individuals. Current definitions of GAD may comprise two different expressions of the disease. There was greater congruence between GAD in either classification system and depression than between DSM-IV/V GAD and ICD-10 GAD, emphasizing the close link between these entities.
Hospital discharge abstracts could be used to identify complications of warfarin if coding for bleeding and thromboembolic events are accurate.
To measure the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9CM) codes for bleeding and thromboembolic diagnoses.
University affiliated, tertiary care hospital in Ottawa, Canada.
A random sample of patients discharged between September 1999 and September 2000 with an ICD-9-CM code indicating a bleeding or thromboembolic diagnosis.
Gold-standard coding was determined by a trained chart abstractor using explicit standard diagnostic criteria for bleeding, major bleeding, and acute thromboembolism. The abstractor was blinded to the original coding. We calculated the sensitivity, specificity, positive, and negative predictive values of the original ICD-9CM codes for bleeding or thromboembolism diagnoses.
We reviewed 616 medical records. 361 patients (59%) had a code indicating a bleeding diagnosis, 291 patients (47%) had a code indicating a thromboembolic diagnosis and 36 patients (6%) had a code indicating both. According to the gold standard criteria, 352 patients experienced bleeding, 333 experienced major bleeding, and 188 experienced an acute thromboembolism. For bleeding, the ICD-9CM codes had the following sensitivity, specificity, positive and negative predictive values [95% CI]: 93% [90-96], 88% [83-91], 91% [88-94], and 91% [87-94], respectively. For major bleeding, the ICD-9CM codes had the following sensitivity, specificity, positive and negative predictive values: 94% [91-96], 83% [78-87], 87% [83-90], and 92% [88-95], respectively. For thromboembolism, the ICD-9CM codes had the following sensitivity, specificity, positive and negative predictive values: 97% [94-99], 74% [70-79], 62% [57-68], and 98% [96-99], respectively. By selecting a sub-group of ICD-9CM codes for thromboembolism, the positive predictive value increased to 87%.
In our centre, the discharge abstract could be used to identify and exclude patients hospitalized with a major bleed or thromboembolism. If coding quality for bleeding is similar in other hospitals, these ICD-9-CM diagnostic codes could be used to study population-based warfarin-associated hemorrhagic complications using administrative databases.
OBJECTIVE: We estimated the accuracy of ICD-10 diagnosis of ovarian cancer in a Danish discharge registry (HDR) by comparing it with Cancer Registry data (DCR). STUDY DESIGN AND SETTING: Patients (N=489) living in North Jutland County, Denmark with ovarian cancer or borderline tumour registered in the HDR or the DCR. We estimated the completeness and positive predictive value (PPV) of ovarian cancer discharge diagnosis. Mortality rates were constructed for both registries. RESULTS: The completeness in the HDR for ovarian cancer was 96% (95% confidence interval [CI]: 94%-98%) and PPV was 87% (95% CI: 85%-90%). 87 (18%) of the patients coded with ovarian cancer in the HDR had borderline tumours. When borderline tumours were excluded from the DCR, the PPV declined to 69% and the completeness did not change. The mortality rate ratio for ovarian cancer registered in the HDR compared to the DCR was 1.08 (95% CI: 0.90-1.29). CONCLUSION: The discharge data (ICD-10) had some misclassification, but can be a valuable tool in assessment of the prognosis of ovarian cancer.
Postpartum hemorrhage (PPH) is recognized as a leading cause of obstetric morbidity and mortality. Population-wide studies have used International Classification of Diseases (ICD) diagnostic codes to track and report the prevalence of PPH. Although the 10th revision (ICD-10) was introduced in Sweden in 1997, the accuracy of ICD-10 codes for PPH is not known. Thus, the aim was to determine the accuracy of diagnostic coding for PPH in the Swedish Pregnancy Register.
We performed a retrospective cohort study of 609 807 deliveries in Sweden between 2014 and 2019. Information on ICD-10 codes for PPH and estimated blood loss were extracted from the Swedish Pregnancy Register. Using an estimated blood loss >1000 mL as the reference standard, we evaluated the diagnostic accuracy of ICD-10 codes for PPH by estimating sensitivity, specificity, positive predictive value and negative predictive value with exact binomial 95% confidence intervals (CIs). In our secondary analysis, we assessed the ICD-10 coding accuracy for severe PPH, defined as an estimated blood loss >1000 mL and transfusion of at least 1 unit of red blood cells registered in the Scandinavian Donations and Transfusion database.
Of the 609 807 deliveries, 43 312 (7.1%) had an ICD-10 code for PPH and 45 071 (7.4%) had an estimated blood loss >1000 mL. The ICD codes had a sensitivity of 88.5% (95% CI 88.2-88.7), specificity of 99.4% (95% CI 99.4-99.4), positive predictive value of 92.0% (95% CI 91.8-92.3) and negative predictive value of 99.1% (95% CI 99.1-99.1). In our secondary analysis, on deliveries with severe PPH, the sensitivity for an ICD code was 91.3% (95% CI 90.7-91.9), whereas specificity was 83.5% (95% CI 82.3-84.6).
Our findings indicate that ICD-10 codes for PPH in Sweden have moderately high sensitivity and excellent specificity. These results suggest that PPH diagnostic codes in medical records and linked pregnancy and birth registers can be used for research, quality improvement and reporting PPH prevalence in Sweden.
The ICD-10 codes are used globally for comparison of diagnoses and complications, and are an important tool for the development of patient safety, healthcare policies and the health economy. The aim of this study was to investigate the accuracy of verified complication rates in surgical admissions identified by ICD-10 codes and to validate these estimates against complications identified using the established Global Trigger Tool (GTT) methodology.
This was a prospective observational study of a sample of surgical admissions in two Norwegian hospitals. Complications were identified and classified by two expert GTT teams who reviewed patients' medical records. Three trained reviewers verified ICD-10 codes indicating a complication present on admission or emerging in hospital.
A total of 700 admissions were drawn randomly from 12 966 procedures. Some 519 possible complications were identified in 332 of 700 admissions (47·4 per cent) from ICD-10 codes. Verification of the ICD-10 codes against information from patients' medical records confirmed 298 as in-hospital complications in 141 of 700 admissions (20·1 per cent). Using GTT methodology, 331 complications were found in 212 of 700 admissions (30·3 per cent). Agreement between the two methods reached 83·3 per cent after verification of ICD-10 codes. The odds ratio for identifying complications using the GTT increased from 5·85 (95 per cent c.i. 4·06 to 8·44) to 25·38 (15·41 to 41·79) when ICD-10 complication codes were verified against patients' medical records.
Verified ICD-10 codes strengthen the accuracy of complication rates. Use of non-verified complication codes from administrative systems significantly overestimates in-hospital surgical complication rates.
Community clinics offer potential for timelier outbreak detection and monitoring than emergency departments. However, the accuracy of syndrome definitions used in surveillance has never been evaluated in community settings. This study's objective was to assess the accuracy of syndrome definitions based on diagnostic codes in physician claims for identifying 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory including influenza-like illness) in community clinics.
We selected a random sample of 3,600 community-based primary care physicians who practiced in the fee-for-service system in the province of Quebec, Canada in 2005-2007. We randomly selected 10 visits per physician from their claims, stratifying on syndrome type and presence, diagnosis, and month. Double-blinded chart reviews were conducted by telephone with consenting physicians to obtain information on patient diagnoses for each sampled visit. The sensitivity, specificity, and positive predictive value (PPV) of physician claims were estimated by comparison to chart review.
1,098 (30.5%) physicians completed the chart review. A chart entry on the date of the corresponding claim was found for 10,529 (95.9%) visits. The sensitivity of syndrome definitions based on diagnostic codes in physician claims was low, ranging from 0.11 (fever) to 0.44 (respiratory), the specificity was high, and the PPV was moderate to high, ranging from 0.59 (fever) to 0.85 (respiratory). We found that rarely used diagnostic codes had a higher probability of being false-positives, and that more commonly used diagnostic codes had a higher PPV.
Future research should identify physician, patient, and encounter characteristics associated with the accuracy of diagnostic codes in physician claims. This would enable public health to improve syndromic surveillance, either by focusing on physician claims whose diagnostic code is more likely to be accurate, or by using all physician claims and weighing each according to the likelihood that its diagnostic code is accurate.
Cites: Proc AMIA Symp. 2001;:164-811833477
Cites: J Am Med Inform Assoc. 2010 Sep-Oct;17(5):595-60120819870
Cites: Ann Intern Med. 2004 Jun 1;140(11):910-2215172906
Cites: Biometrics. 1983 Mar;39(1):207-156871349
Cites: Med Care. 1991 Oct;29(10):977-881921530
Cites: Am J Epidemiol. 1994 Oct 15;140(8):759-697942777
Cites: CMAJ. 1998 Sep 8;159(5):525-89757182
Cites: J Fam Pract. 1998 Nov;47(5):366-99834772
Cites: J Am Med Inform Assoc. 2005 Nov-Dec;12(6):618-2916049227
Diagnostic accuracy is crucial not only for prognostic and therapeutic reasons, but also for epidemiologic studies. We aimed to study the accuracy of the clinical diagnosis of Parkinson disease (PD) for participants in The Nord-Trøndelag Health Study (HUNT), a health survey, containing data from approximately 126,000 individuals and biological material from 80,000 individuals. We included 980 participants from the HUNT study diagnosed with PD or secondary parkinsonism/related parkinsonian disorders. The participants had been diagnosed in conjunction with admission to hospitals in Trøndelag or through out-patient examination. We validated the diagnosis of PD by reviewing available Electronic Health Records (EHRs) using the MDS Clinical Diagnostic Criteria as gold standard. In total 61% (601/980) of the participants had available EHRs and were selected for validation. Out of those, 92% (550/601) had been diagnosed with PD while 8% (51/601) had been diagnosed with secondary parkinsonism/related parkinsonian disorders. The main outcome measure was the accuracy of the clinical diagnosis of PD for participants in the HUNT study. We verified PD in 65% (358/550) and excluded PD in 35% (192/550) of the participants. According to our results, the overall quality of the clinical diagnosis of PD for participants in the HUNT study is not optimal. Quality assurance of ICD codes entered into health registers is crucial before biological material obtained from these populations can be used in the search of new biomarkers for PD.
Administrative discharge codes are widely used in epidemiology, but the specificity and sensitivity of this coding is unknown and must be validated. We assessed the validity of the discharge diagnosis of syncope in administrative registers and reviewed the etiology of syncope after workup.
Two samples were investigated. One sample consisted of 5262 randomly selected medical patients. The other sample consisted of 750 patients admitted or seen in the emergency department (ED) for syncope (ICD-10: R55.9) in three hospitals in Denmark. All charts were reviewed for baseline characteristics and to confirm the presence/absence of syncope and to compare with the administrative coding. In a sample of 600 admitted patients 570 (95%) and of 150 patients from ED 140 (93%) had syncope representing the positive predictive values. Median age of the population was 69 years (IQR: ± 14). In the second sample of 5262 randomly selected medical patients, 75 (1.4%) had syncope, of which 47 were coded as R55.9 yielding a sensitivity of 62.7%, a negative predictive value of 99.5%, and a specificity of 99.9%.
ED and hospital discharge diagnostic coding for syncope has a positive predictive value of 95% and a sensitivity of 63%.
Studies of achalasia epidemiology are important as they often yield new insights into disease etiology. In this study, our objective was to carry out the first North American population-based study of achalasia epidemiology using a governmental administrative database.
All residents in the province of Alberta, Canada receive universal healthcare coverage as a benefit. The provincial health ministry, Alberta Health and Wellness, maintains a central stakeholder database of patient demographic information and physician billing claims. We defined an achalasia case as a billing claim submitted for the years 1996-2007 with an ICD-9-CM code of 530.0 or 530 and a Canadian Classification of Procedure treatment code of 54.92A (endoscopic balloon dilation) or 54.6 (esophagomyotomy). A preliminary validation study of the case definition demonstrated a sensitivity of 85% and specificity of 99% for known cases and controls.
A total of 463 achalasia cases were identified from 1995 to 2008 (59.6% males). Mean age at diagnosis was 53.1 years. In 2007, the achalasia incidence was 1.63/100,000 (95% CI 1.20, 2.06) and the prevalence was 10.82/100,000 (95% CI 9.70, 11.93). We observed a steady increase in the overall prevalence rate from 2.51/100,000 in 1996 to 10.82/100,000 in 2007. Survival of achalasia cases was significantly less than age-sex matched population controls (P
Cohort studies often report findings on children with Attention Deficit Hyperactivity Disorder (ADHD) but may be biased by self-selection. The representativeness of cohort studies needs to be investigated to determine whether their findings can be generalised to the general child population. The aim of the present study was to examine the representativeness of child ADHD in the Norwegian Mother and Child Cohort Study (MoBa).
The study population was children born between January 1, 2000 and December 31, 2008 registered with hyperkinetic disorders (hereafter ADHD) in the Norwegian Patient Registry during the years 2008-2013, and two groups of children with ADHD were identified in: 1. MoBa and 2. The general child population. We used the multiaxial International Classification of Diseases (ICD-10) and compared the proportions of comorbid disorders (axes I-III), abnormal psychosocial situations (axis V) and child global functioning (axis VI) between these two groups. We also compared the relative differences in the multiaxial classifications for boys and girls and for children with/without axis I comorbidity, respectively in these two groups of children with ADHD.
A total of 11 119 children were registered with ADHD, with significantly fewer in MoBa (1.45%) than the general child population (2.11%), p