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Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.

ABSTRACT: BACKGROUND:Determining the primary indication of a surgical procedure can be useful in identifying patients undergoing elective surgery where shared decision-making is recommended. The purpose of this study was to develop and validate an algorithm to identify patients receiving the following combinations of surgical procedure and primary indication as part of a study to promote shared decision-making: (1) knee arthroplasty to treat knee osteoarthritis (KOA); (2) hip arthroplasty to treat hip osteoarthritis (HOA); (3) spinal surgery to treat lumbar spinal stenosis (SpS); and (4) spinal surgery to treat lumbar herniated disc (HD). METHODS:Consecutive surgical procedures performed by participating spine, hip, and knee surgeons at four sites within an integrated care network were included. Study staff reviewed electronic medical records to ascertain a "gold standard" determination of the procedure and primary indication status. Electronic algorithms consisting of ICD-10 and CPT codes for each combination of procedure and indication were then applied to records for each case. The primary measures of validity for the algorithms were the sensitivity and specificity relative to the gold standard review. RESULTS:Participating surgeons performed 790 procedures included in this study. The sensitivity of the algorithms in determining whether a surgical case represented one of the combinations of procedure and primary indication ranged from 0.70 (HD) to 0.92 (KOA). The specificity ranged from 0.94 (SpS) to 0.99 (HOA, KOA). CONCLUSION:The electronic algorithm was able to identify all four procedure/primary indication combinations of interest with high specificity. Additionally, the sensitivity for the KOA cases was reasonably high. For HOA and the spine conditions, additional work is needed to improve the sensitivity of the algorithm to identify the primary indication for each case.

SUBMITTER: Giardina JC

PROVIDER: S-EPMC7425151 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.

Giardina John C JC Cha Thomas T Atlas Steven J SJ Barry Michael J MJ Freiberg Andrew A AA Leavitt Lauren L Marques Felisha F Sepucha Karen K

BMC medical informatics and decision making 20200812 1

<h4>Background</h4>Determining the primary indication of a surgical procedure can be useful in identifying patients undergoing elective surgery where shared decision-making is recommended. The purpose of this study was to develop and validate an algorithm to identify patients receiving the following combinations of surgical procedure and primary indication as part of a study to promote shared decision-making: (1) knee arthroplasty to treat knee osteoarthritis (KOA); (2) hip arthroplasty to treat ...[more]

PMID: 32787849

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Project description:STUDY DESIGNRetrospective analysis of Medicare claims linked to a multicenter clinical trial.OBJECTIVEThe Spine Patient Outcomes Research Trial (SPORT) provided a unique opportunity to examine the validity of a claims-based algorithm for grouping patients by surgical indication. SPORT enrolled patients for lumbar disc herniation, spinal stenosis, and degenerative spondylolisthesis. We compared the surgical indication derived from Medicare claims with that provided by SPORT surgeons, the "gold standard."SUMMARY OF BACKGROUND DATAAdministrative data are frequently used to report procedure rates, surgical safety outcomes, and costs in the management of spinal surgery. However, the accuracy of using diagnosis codes to classify patients by surgical indication has not been examined.METHODSMedicare claims were link to beneficiaries enrolled in SPORT. The sensitivity and specificity of 3 claims-based approaches to group patients on the basis of surgical indications were examined: (1) using the first listed diagnosis; (2) using all diagnoses independently; and (3) using a diagnosis hierarchy on the basis of the support for fusion surgery.RESULTSMedicare claims were obtained from 376 SPORT participants, including 21 with disc herniation, 183 with spinal stenosis, and 172 with degenerative spondylolisthesis. The hierarchical coding algorithm was the most accurate approach for classifying patients by surgical indication, with sensitivities of 76.2%, 88.1%, and 84.3% for disc herniation, spinal stenosis, and degenerative spondylolisthesis cohorts, respectively. The specificity was 98.3% for disc herniation, 83.2% for spinal stenosis, and 90.7% for degenerative spondylolisthesis. Misclassifications were primarily due to codes attributing more complex pathology to the case.CONCLUSIONStandardized approaches for using claims data to group patients accurately by surgical indications have widespread interest. We found that a hierarchical coding approach correctly classified more than 90% of spine patients into their respective SPORT cohorts. Therefore, claims data seem to be a reasonably valid approach to classifying patients by surgical indication.LEVEL OF EVIDENCE3.

Project description:BackgroundChildhood cancer relies heavily on inpatient hospital services to deliver tumor-directed therapy and manage toxicities. Hospitalizations have increased over the past decade, though not uniformly across childhood cancer diagnoses. Analysis of the reasons for admission of children with cancer could enhance comparison of resource use between cancers, and allow clinical practice data to be interpreted more readily. Such comparisons using nationwide data sources are difficult because of numerous subdivisions in the International Classification of Diseases Clinical Modification (ICD-9) system and inherent complexities of treatments. This study aimed to develop a systematic approach to classifying cancer-related admissions in administrative data into categories that reflected clinical practice and predicted resource use.MethodsWe developed a multistep algorithm to stratify indications for childhood cancer admissions in the Kids Inpatient Databases from 2003, 2006 and 2009 into clinically meaningful categories. This algorithm assumed that primary discharge diagnoses of cancer or cytopenia were insufficient, and relied on procedure codes and secondary diagnoses in these scenarios. Clinical Classification Software developed by the Healthcare Cost and Utilization Project was first used to sort thousands of ICD-9 codes into 5 mutually exclusive diagnosis categories and 3 mutually exclusive procedure categories, and validation was performed by comparison with the ICD-9 codes in the final admission indication. Mean cost, length of stay, and costs per day were compared between categories of indication for admission.ResultsA cohort of 202,995 cancer-related admissions was grouped into four categories of indication for admission: chemotherapy (N=77,791, 38%), to undergo a procedure (N=30,858, 15%), treatment for infection (N=30,380, 15%), or treatment for other toxicities (N=43,408, 21.4%). The positive predictive value for the algorithm was >95% for each category. Admissions for procedures had higher mean hospital costs, longer hospital stays, and higher costs per day compared with other admission reasons (p<0.001).ConclusionsThis is the first description of a method for grouping indications for childhood cancer admission within an administrative dataset into clinically relevant categories. This algorithm provides a framework for more detailed analyses of pediatric hospitalization data by cancer type.

Project description:BackgroundAccurate determination of colonoscopy indication is required for managing clinical programs and performing research; however, existing algorithms that use available electronic databases (eg, diagnostic and procedure codes) have yielded limited accuracy.ObjectiveTo develop and validate an algorithm for classifying colonoscopy indication that uses comprehensive electronic medical data sources.DesignWe developed an algorithm for classifying colonoscopy indication by using commonly available electronic diagnostic, pathology, cancer, and laboratory test databases and validated its performance characteristics in comparison with a comprehensive review of patient medical records. We also evaluated the influence of each data source on the algorithm's performance characteristics.SettingKaiser Permanente Northern California healthcare system.PatientsA total of 300 patients who underwent colonoscopy between 2007 and 2010.InterventionsColonoscopy.Main outcome measurementsAlgorithm's sensitivity, specificity, and positive predictive value (PPV) for classifying screening, surveillance, and diagnostic colonoscopies. The reference standard was the indication assigned after comprehensive medical record review.ResultsFor screening indications, the algorithm's sensitivity was 88.5% (95% confidence interval [CI], 80.4%-91.7%), specificity was 91.7% (95% CI, 87.0%-95.1%), and PPV was 83.3% (95% CI, 74.7%-90.0%). For surveillance indications, the algorithm's sensitivity was 93.4% (95% CI, 86.2%-97.5%), specificity was 92.8% (95% CI, 88.4%-95.9%), and PPV was 85.0% (95% CI, 76.5%-91.4%). The algorithm's sensitivity, specificity, and PPV for diagnostic indications were 81.4% (95% CI, 73.0%-88.1%), 96.8% (95% CI, 93.2%-98.8%), and 93.9% (95% CI, 87.2%-97.7%), respectively.LimitationsValidation was confined to a single healthcare system.ConclusionAn algorithm that uses commonly available modern electronic medical data sources yielded a high sensitivity, specificity, and PPV for classifying screening, surveillance, and diagnostic colonoscopy indications. This algorithm had greater accuracy than the indication listed on the colonoscopy report.

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Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.

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Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.

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