Objectives: (a) to estimate the accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for amyotrophic lateral sclerosis (ALS) in the Hospital Discharge Database (HDD) of the Italian region Friuli-Venezia Giulia; (b) to identify the predictors of a true positive ALS code; (c) to compare incident and prevalent cases obtained from HDD with those identified in a retrospective population-based study. Methods: Records of all patients discharged 2010-2014 with an ICD-9-CM code for ALS and other motor neuron diseases were extracted from the HDD. For each record, all the available clinical documentation was evaluated to confirm or reject the diagnosis of ALS. ALS incident and prevalent cases were identified. Validity measures were calculated both overall and stratified by patient and hospitalization characteristics. Adjusted odds ratio (aOR), with 95% confidence interval (95%CI), of a true positive code was estimated using unconditional logistic regression. Results: ALS code had sensitivity 92.9%, specificity 75.3%, positive predictive value (PPV) 92.3%, and negative predictive value (NPV) 76.8%. A true positive ALS code was predicted by concurrent codes for respiratory interventions (aOR: 3.82; 95%CI: 2.09-6.99), primary position code (2.78; 1.68-4.62), non-programed hospitalization (2.06; 1.18-3.61), male patient (1.56; 1.06-2.29), and hospitalization length <14 days (1.42; 1.07-2.84). Two hundred and thirty-six prevalent and 187 incident cases were identified, 84% of those detected in the population-based study. Conclusion: ALS code shows very good accuracy and identifies a high percentage of true positive, incident and prevalent cases, but additional sources and an algorithm based on selected variables may further improve case identification.
Validation of discharge diagnosis coding for amyotrophic lateral sclerosis in an Italian regional healthcare database
Francesca Palese
Primo
;
2020-01-01
Abstract
Objectives: (a) to estimate the accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for amyotrophic lateral sclerosis (ALS) in the Hospital Discharge Database (HDD) of the Italian region Friuli-Venezia Giulia; (b) to identify the predictors of a true positive ALS code; (c) to compare incident and prevalent cases obtained from HDD with those identified in a retrospective population-based study. Methods: Records of all patients discharged 2010-2014 with an ICD-9-CM code for ALS and other motor neuron diseases were extracted from the HDD. For each record, all the available clinical documentation was evaluated to confirm or reject the diagnosis of ALS. ALS incident and prevalent cases were identified. Validity measures were calculated both overall and stratified by patient and hospitalization characteristics. Adjusted odds ratio (aOR), with 95% confidence interval (95%CI), of a true positive code was estimated using unconditional logistic regression. Results: ALS code had sensitivity 92.9%, specificity 75.3%, positive predictive value (PPV) 92.3%, and negative predictive value (NPV) 76.8%. A true positive ALS code was predicted by concurrent codes for respiratory interventions (aOR: 3.82; 95%CI: 2.09-6.99), primary position code (2.78; 1.68-4.62), non-programed hospitalization (2.06; 1.18-3.61), male patient (1.56; 1.06-2.29), and hospitalization length <14 days (1.42; 1.07-2.84). Two hundred and thirty-six prevalent and 187 incident cases were identified, 84% of those detected in the population-based study. Conclusion: ALS code shows very good accuracy and identifies a high percentage of true positive, incident and prevalent cases, but additional sources and an algorithm based on selected variables may further improve case identification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
