Exploring patterns of recurrent melanoma in Northeast Scotland to inform the introduction a digital self-examination intervention

Peer reviewedPublisher PD

Development of a multi-phase claims-based algorithm for pregnancy research

Background: Medication use during pregnancy may lead to birth defects or other complications. Safety studies are critical, with registries being common but often yielding a small number of cases after years of follow-up. Administrative claims data can be used to study large numbers of women and infants more quickly and efficiently, if these claims data accurately identify the following: pregnancy outcomes, gestational age, drug exposure by trimester, and mother/infant links. No single existing algorithm uses only administrative claims data to measure all of these variables. Objectives: Develop a multi-phase algorithm for use in administrative claims data to identify live/nonlive pregnancy outcomes (phase 1), estimate gestational age (phase 2), estimate drug exposure by trimester (phase 3), and link claims data for mothers and infants (phase 4). Methods: A multi-phase algorithm is being developed in a phased manner among women aged ≥15 and ≤50 years with ≥1 end of pregnancy (EOP) ICD-9 code with enrollment and prescription coverage 340 days prior to the end of pregnancy in the Henry Ford Health System between 1/1/2013 to 9/30/2015. In all phases, algorithms will be developed, applied to claims data, and compared to electronic medical records for validation. The best performing algorithm will be used in the next phase. For phase 1, we developed 3 algorithms: Alg1: ≥1 definitive ICD-9 EOP code; Alg2: ≥1 ICD-9 EOP code in the primary position; and Alg3: ≥1 ICD-9 EOP code in the primary position—and—≥1 procedure code. The positive predictive value (PPV), sensitivity, and 95% confidence intervals (CI) were calculated. Results: A total of 698 women met inclusion criteria. In phase 1, the number of women and live births (LB) were as follows: Alg1—674 women, 529 LB; Alg2—658 women, 522 LB; and Alg3—589 women, 520 LB. (Nonlive outcomes not presented.) Overall algorithm PPV and sensitivity (95% CI in parens) were: Alg1—94% (CI: 92–96%); 99% (CI: 98–100%); Alg2—91% (88–93%); 97% (CI: 95–98%); Alg3—93% (CI: 90–95%); 87% (CI: 84–89%). Live outcomes PPV and sensitivity were: Alg1—98% (CI: 97–99%); 97% (CI: 96–99%); Alg2—92% (CI: 89–94%); 98% (CI: 97–99%); Alg3—93% (CI: 90–95%); 98% (CI: 96–99%). Nonlive outcomes PPV and sensitivity were: Alg1—79% (CI: 71–85%); 99% (CI: 95–100%); Alg2—72% (CI: 64–79%); 85 % (CI: 77–91%); Alg3—72% (CI: 60–83%); 41% (CI: 32–51%). Conclusions: End of pregnancy outcomes can be identified in claims data with a high PPV and sensitivity. Further analyses are underway in the Alg1 cohort to develop algorithms for phases 2–4

Can valid cases of schizophrenia be identified in administrative claims data?

Background: Large data sources, such as administrative claims, can be used to better understand the natural history, treatment and outcomes of schizophrenia provided that valid cases can be identified. International Classification of Diseases (ICD) codes or a combination of ICD codes and prescription claims have been used to identify schizophrenia patients, but validation studies of these methods for schizophrenia are limited. Objectives: To determine if valid cases of patients with schizophrenia can be identified using administrative claims data. Methods: Claims data from the Henry Ford Health System, an integrated healthcare system serving metropolitan Detroit, Michigan, were used to identify patients aged 18-64 years with schizophrenia from 01/ 01/2009 to 06/30/2014. Potential cases had ≥2 ICD-9 codes (295.x) for schizophrenia disorder in any position, ≥2 claims for an antipsychotic medication, ≥12 months of continuous enrollment pre-index, and ≥6 months of continuous enrollment post-index. Index date was defined as the first 295.x ICD-9 code. Patients with organic cognitive decline or schizoaffective disorder independent of schizophrenia were excluded. Trained medical records abstractors performed a structured review of all relevant fields including inpatient and outpatient records of the electronic medical record (EMR) (e.g. diagnosis fields; free text) to verify the schizophrenia diagnosis ±12 months from the index date. Results: Of the 145 patients who met inclusion/exclusion criteria, EMR review was completed on a random sample of 111 patients. Of these, 65 had an EMR-confirmed diagnosis of schizophrenia for a positive predictive value (PPV) of 59% (95% confidence interval: 52-64%). Unconfirmed patients had diagnoses of bipolar disorder (N = 25; 54%), major depressive disorder (N = 28; 61%), and/or schizoaffective disorder (N = 3; 7%). These diagnoses may be comorbid with a schizophrenia diagnosis, but no schizophrenia diagnosis was recorded. Conclusions: Identifying valid cases of schizophrenia in administrative claims data is challenging. There are few published studies of validated claims-based algorithms that identify cases of treated schizophrenia. This study, requiring ≥2 ICD-9 codes and ≥2 prescription claims, did not yield a high PPV for schizophrenia. Reasons may include diagnostic challenges in differentiating psychiatric conditions or comorbid diagnoses where only 1 diagnosis is recorded. Future studies of validated algorithms to identify schizophrenia patients are warranted

Developing a multi-phase claims-based algorithm to facilitate the study of drug exposure during pregnancy

Background/Purpose: The use of antirheumatic medications during pregnancy may lead to birth defects or other complications. Safety studies are critical, with registries being common but often yielding a small number of cases after years of follow-up. Medical administrative claims data can be used to study large numbers of women and infants more quickly and efficiently, if these claims data accurately identify: pregnancy outcomes, gestational age, drug exposure by trimester and mother/infant links. No single existing algorithm uses only administrative claims data to measure all of these variables. The objective is to develop a multi-phase algorithm for use in administrative medical claims data to identify live/non-live pregnancy outcomes (phase 1), estimate gestational age (phase 2), estimate drug exposure by trimester (phase 3) and link claims data for mothers and infants (phase 4). Methods: A multi-phase algorithm is being developed in a phased manner among women aged ≥15 and ≤50 years with ≥1 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) end of pregnancy code. Women were enrolled and had prescription coverage 340 days prior to the end of pregnancy in the Henry Ford Health System between 1/1/2013 and 9/30/2015. In all phases, algorithms will be developed, applied to claims data and compared to electronic medical records for validation. Positive predictive value (PPV), sensitivity and 95% CI will be calculated. The best performing algorithm developed in each phase will be used to move to the next phase. Results: A total of 698 women met inclusion criteria. Three algorithms were developed and tested for phase 1. Algorithm 1 (≥1 definitive ICD-9-CM end of pregnancy code) performed best (Table 1). Two algorithms for phase 2 were developed and tested. Algorithm 2 (adjusted delivery date based on selected procedure codes and assigned 245 days to preterm, 273 days to term, and 294 days to post-term) performed best for preterm and term births (Table 2). Conclusion: End of pregnancy outcomes can be identified in claims data with high PPV and sensitivity. Gestational age can be estimated with reasonable PPV and sensitivity for preterm and term live births. Further analyses are underway for phases 3 and 4

A multiphase claims-based algorithm for live pregnancy outcomes research

Background: Safety studies on outcomes of medication exposure during pregnancy are critical. Administrative claims data could be used however, these studies are often unfeasible due to the difficulty in identifying pregnancies, estimating gestational age (GA), estimating exposure during pregnancy and linking mothers to infants using only claims data. Objectives: To develop a multiphase claims-based algorithm that identifies pregnancy outcomes (Phase [Ph]1), estimates GA (Ph2), estimates medication exposure (Ph3) and links mothers to infants (Ph4). Methods: A multiphase algorithm was developed for use in women aged ≥15 and ≤50 years with ≥1 end of pregnancy (EOP) ICD-9 code and enrolment and prescription coverage 340 days prior to EOP in the Henry Ford Health System in the United States between 1 January 2013 and 30 September 2015. Algorithms were developed, applied to claims data and validated against electronic medical records. Positive predictive value (PPV), sensitivity (Sens) and 95% CI were calculated. The best-performing algorithm in each phase was used in the next phase. Ph1 results were presented at ICPE 2017 (abstract 154). Two Ph2 algorithms estimated GA at live birth as pre-term (245 days), term (273 days) or post-term (294 days) using EOP ICD-9 code dates adjusted with the dates of obstetric procedure codes. Three Ph3 algorithms evaluated exposure to long-term (eg, antidepressants) and short-term (antibiotics) medications overall and by trimester (using Ph2 estimated GA) using fill date and one of the following: days\u27 supply (DS), DS plus a 14-day grace period or DS plus medication-specific grace periods. Three Ph4 algorithms assessed mother/infant linkage using combinations of birth/delivery dates, 3-digit ZIP codes, caesarean birth codes, delivery status (eg, singleton) and a screening hierarchy. Results: Ph2 algorithms performed identically for term births (PPV: 94% [95% CI: 91, 96] Sens: 81% [76, 84]) and post-term births (PPV: 40% [32, 49] Sens: 90% [79, 96]). Algorithm 2 performed better for pre-term births (PPV: 94% [79, 99] Sens: 68% [52, 81]). Using Ph2 Algorithm 2, all Ph3 algorithms performed identically overall (PPV: 100% [98, 100] Sens: 100% [98, 100]). The lowest PPV for long-or short-term drug exposure by trimester was 91% (77, 98) (Sens: 100% [89, 100]). Ph4 algorithms had approximately 60% PPV. Work is ongoing to improve Ph4 algorithm performance. Conclusions: A multiphase algorithm can be used with claims data to estimate GA at live birth and exposure to medications during pregnancy

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

PURPOSE: Large numbers of multiple myeloma patients can be studied in real-world clinical settings using administrative databases. The validity of these studies is contingent upon accurate case identification. Our objective was to develop and evaluate algorithms to use with administrative data to identify multiple myeloma cases. METHODS: Patients aged ≥18 years with ≥1 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for multiple myeloma (203.0x) were identified at two study sites. At site 1, several algorithms were developed and validated by comparing results to tumor registry cases. An algorithm with a reasonable positive predictive value (PPV) (0.81) and sensitivity (0.73) was selected and then validated at site 2 where results were compared with medical chart data. The algorithm required that ICD-9-CM codes 203.0x occur before and after the diagnostic procedure codes for multiple myeloma. RESULTS: At site 1, we identified 1432 patients. The PPVs of algorithms tested ranged from 0.54 to 0.88. Sensitivities ranged from 0.30 to 0.88. At site 2, a random sample (n = 400) was selected from 3866 patients, and medical charts were reviewed by a clinician for 105 patients. Algorithm PPV was 0.86 (95% CI, 0.79-0.92). CONCLUSIONS: We identified cases of multiple myeloma with adequate validity for claims database analyses. At least two ICD-9-CM diagnosis codes 203.0x preceding diagnostic procedure codes for multiple myeloma followed by ICD-9-CM codes within a specific time window after diagnostic procedure codes were required to achieve reasonable algorithm performance