Code-based Algorithms Research Articles

Improving Administrative Code-Based Algorithms for Sepsis Surveillance*

Improving Administrative Code-Based Algorithms for Sepsis Surveillance*

Adjudication of Codes for Identifying Sepsis in Hospital Administrative Data by Expert Consensus*

OBJECTIVES: Refine the administrative data definition of sepsis in hospitalized patients, including less severe cases. Design and Setting: For each of 1928 infection and 108 organ dysfunction codes used in Canadian hospital abstracts, experts reached consensus on the likelihood that it could relate to sepsis. We developed a new algorithm, called AlgorithmL, that requires at least one infection and one organ dysfunction code adjudicated as likely or very likely to be related to sepsis. AlgorithmL was compared with four previously described algorithms, regarding included codes, population-based incidence, and hospital mortality rates—separately for ICU and non-ICU cohorts in a large Canadian city. We also compared sepsis identification from these code-based algorithms with the Centers for Disease Control’s Adult Sepsis Event (ASE) definition. SUBJECTS: Among Calgary’s adult population of 1.033 million there were 61,632 eligible hospitalizations. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: AlgorithmL includes 720 infection codes and 50 organ dysfunction codes. Comparison algorithms varied from 42–941 infection codes to 2–36 organ codes. There was substantial nonoverlap of codes in AlgorithmL vs. the comparators. Annual sepsis incidence rates (per 100,000 population) based on AlgorithmL were 91 in the ICU and 291 in the non-ICU cohort. Incidences based on comparators ranged from 28–77 for ICU to 11–266 for non-ICU cohorts. Hospital sepsis mortality rates based on AlgorithmL were 24% in ICU and 17% in non-ICU cohorts; based on comparators, they ranged 27–38% in the ICU cohort and 18–47% for the non-ICU cohort. Of AlgorithmL-identified cases, 41% met the ASE criteria, compared with 42–82% for the comparator algorithms. CONCLUSIONS: Compared with other code-based algorithms, AlgorithmL includes more infection and organ dysfunction codes. AlgorithmL incidence rates are higher; hospital mortality rates are lower. AlgorithmL may more fully encompass the full range of sepsis severity.

Structural analysis of code-based algorithms of the NIST post-quantum call

Abstract Code-based cryptography is currently the second most promising post-quantum mathematical tool for quantum-resistant algorithms. Since in 2022 the first post-quantum standard Key Encapsulation Mechanism, Kyber (a latticed-based algorithm), was selected to be established as standard, and after that the National Institute of Standards and Technology post-quantum standardization call focused in code-based cryptosystems. Three of the four candidates that remain in the fourth round are code-based algorithms. In fact, the only non-code-based algorithm (SIKE) is now considered vulnerable. Due to this landscape, it is crucial to update previous results about these algorithms and their functioning. The Fujisaki-Okamoto transformation is a key part of the study of post-quantum algorithms and in this work we focus our analysis on Classic McEliece, BIKE and HQC proposals, and how they apply this transformation to obtain IND-CCA semantic security. Since after security the most important parameter in the evaluation of the algorithms is performance, we have compared the performance of the code-based algorithms of the NIST call considering the same architecture for all of them.

A provably masked implementation of BIKE Key Encapsulation Mechanism

BIKE is a post-quantum key encapsulation mechanism (KEM) selected for the 4th round of the NIST's standardization campaign. It relies on the hardness of the syndrome decoding problem for quasi-cyclic codes and on the indistinguishability of the public key from a random element, and provides the most competitive performance among round 4 candidates, which makes it relevant for future real-world use cases. Analyzing its side-channel resistance has been highly encouraged by the community and several works have already outlined various side-channel weaknesses and proposed ad-hoc countermeasures. However, in contrast to the well-documented research line on masking lattice-based algorithms, the possibility of generically protecting code-based algorithms by masking has only been marginally studied in a 2016 paper by Chen et al. in SAC 2015. At this stage of the standardization campaign, it is important to assess the possibility of fully masking BIKE scheme and the resulting cost in terms of performances. In this work, we provide the first high-order masked implementation of a code-based algorithm. We had to tackle many issues such as finding proper ways to handle large sparse polynomials, masking the key-generation algorithm or keeping the benefit of the bitslicing. In this paper, we present all the gadgets necessary to provide a fully masked implementation of BIKE, we discuss our different implementation choices and we propose a full proof of masking in the Ishai Sahai and Wagner (Crypto 2003) model. More practically, we also provide an open C-code masked implementation of the key-generation, encapsulation and decapsulation algorithms with extensive benchmarks. While the obtained performance is slower than existing masked lattice-based algorithms, we show that masking at order 1, 2, 3, 4 and 5 implies a performance penalty of x5.8, x14.2, x24.4, x38 and x55.6 compared to order 0 (unmasked and unoptimized BIKE). This scaling is encouraging and no Boolean to Arithmetic conversion has been used.

Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

Best Practices for Identifying Hospitalized Lower Respiratory Tract Infections Using Administrative Data: A Systematic Literature Review of Validation Studies.

Estimating the burden of lower respiratory tract infections (LRTIs) increasingly relies on administrative databases using International Classification of Diseases (ICD) codes, but no standard methodology exists. We defined best practices for ICD-based algorithms that estimate LRTI incidence in adults. We conducted a systematic review of validation studies assessing the use of ICD code-based algorithms to identify hospitalized LRTIs in adults, published in Medline, EMBASE, and LILACS between January 1996 and January 2022, according to PRISMA guidelines. We assessed sensitivity, specificity, and other accuracy measures of different algorithms. We included 26 publications that used a variety of ICD code-based algorithms and gold standard criteria, and 18 reported sensitivity and/or specificity. Sensitivity was below 80% in 72% (38/53) of algorithms and specificity exceeded 90% in 77% (37/48). Algorithms for all-cause LRTI (n = 18) that included only pneumonia codes in primary position (n = 3) had specificity greater than 90% but low sensitivity (55-72%). Sensitivity increased by 5-15%, with minimal loss in specificity, with the addition of primary codes for severe pneumonia (e.g. sepsis) while pneumonia codes were in secondary position, and by 13% with codes from LRTI-related infections (e.g. viral) or other respiratory diseases (e.g. empyema). Sensitivity increased by 8% when pneumonia codes were in any position, but specificity was not reported. In hospital-acquired pneumonia and pneumococcal-specific pneumonia, algorithms containing only nosocomial- or pathogen-specific ICD codes had poor sensitivity, which improved when broader pneumonia codes were added, in particular codes for unspecified organisms. Our systematic review highlights that most ICD code-based algorithms are relatively specific, but miss a substantial number of hospitalized LRTI adult cases. Best practices to estimate LRTI incidence in this population include the use of all pneumonia ICD codes for any LRTI outcome and, to a lesser extent, those for other LRTI-related infections or respiratory diseases.

Validation of human immunodeficiency virus diagnosis codes among women enrollees of a U.S. health plan

BackgroundEfficiently identifying patients with human immunodeficiency virus (HIV) using administrative health care data (e.g., claims) can facilitate research on their quality of care and health outcomes. No prior study has validated the use of only ICD-10-CM HIV diagnosis codes to identify patients with HIV.MethodsWe validated HIV diagnosis codes among women enrolled in a large U.S. integrated health care system during 2010–2020. We examined HIV diagnosis code-based algorithms that varied by type, frequency, and timing of the codes in patients’ claims data. We calculated the positive predictive values (PPVs) and 95% confidence intervals (CIs) of the algorithms using a medical record-confirmed diagnosis of HIV as the gold standard.ResultsA total of 272 women with ≥ 1 HIV diagnosis code in the administrative claims data were identified and medical records were reviewed for all 272 women. The PPV of an algorithm classifying women as having HIV as of the first HIV diagnosis code during the observation period was 80.5% (95% CI: 75.4–84.8%), and it was 93.9% (95% CI: 90.0-96.3%) as of the second. Little additional increase in PPV was observed when a third code was required. The PPV of an algorithm based on ICD-10-CM-era codes was similar to one based on ICD-9-CM-era codes.ConclusionIf the accuracy measure of greatest interest is PPV, our findings suggest that use of ≥ 2 HIV diagnosis codes to identify patients with HIV may perform well. However, health care coding practices may vary across settings, which may impact generalizability of our results.

Clinical evaluation of code-based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases.

Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code-based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H-specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well-defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best-performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code-based PAH algorithms to their research question and demonstrate the importance of including well-defined temporal components in the algorithms.

Code-Based Algorithms for Identifying Dementia in Electronic Health Records: Bridging the Gap Between Theory and Practice.

Code-based algorithms are crucial tools in the detection of dementia using electronic health record data, with broad applications in medical research and healthcare. Vassilaki et al.'s study explores the efficacy of code-based algorithms in dementia detection using electronic health record data, achieving approximately 70% sensitivity and positive predictive value. Despite the promising results, the algorithms fail to detect around 30% of dementia cases, highlighting challenges in distinguishing cognitive decline factors. The study emphasizes the need for algorithmic improvements and further exploration across diverse healthcare systems and populations, serving as a critical step toward bridging gaps in dementia care and understanding.

Enhancement and external validation of algorithms using diagnosis codes to identify invasive Escherichia coli disease

Objective To assess the predictive accuracy of code-based algorithms for identifying invasive Escherichia coli (E. coli) disease (IED) among inpatient encounters in US hospitals. Methods The PINC AI Healthcare Database (10/01/2015–03/31/2020) was used to assess the performance of six published code-based algorithms to identify IED cases among inpatient encounters. Case-confirmed IEDs were identified based on microbiological confirmation of E. coli in a normally sterile body site (Group 1) or in urine with signs of sepsis (Group 2). Code-based algorithm performance was assessed overall, and separately for Group 1 and Group 2 based on sensitivity, specificity, positive and negative predictive value (PPV and NPV) and F1 score. The improvement in performance of refinements to the best-performing algorithm was also assessed. Results Among 2,595,983 encounters, 97,453 (3.8%) were case-confirmed IED (Group 1: 60.9%; Group 2: 39.1%). Across algorithms, specificity and NPV were excellent (>97%) for all but one algorithm, but there was a trade-off between sensitivity and PPV. The algorithm with the most balanced performance characteristics included diagnosis codes for: (1) infectious disease due to E. coli OR (2) sepsis/bacteremia/organ dysfunction combined with unspecified E. coli infection and no other concomitant non-E. coli invasive disease (sensitivity: 56.9%; PPV: 56.4%). Across subgroups, the algorithms achieved lower algorithm performance for Group 2 (sensitivity: 9.9%–61.1%; PPV: 3.8%–16.0%). Conclusions This study assessed code-based algorithms to identify IED during inpatient encounters in a large US hospital database. Such algorithms could be useful to identify IED in healthcare databases that lack information on microbiology data.

Defining major surgical complications using administrative data in Ontario: a validation study.

Although surgical complications are often included as an outcome of surgical research conducted using administrative data, little validation work has been performed. We sought to evaluate the diagnostic performance of an algorithm designed to capture major surgical complications using health administrative data. This retrospective study included patients who underwent high-risk elective general surgery at a single institution in Ontario, Canada, from Sept. 1, 2016, to Sept. 1, 2017. Patients were identified for inclusion using the local operative database. Medical records were reviewed by trained clinicians to abstract postoperative complications. Data were linked to administrative data holdings, and a series of code-based algorithms were applied to capture a composite indicator of major surgical complications. We used sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy to evaluate the performance of our administrative data algorithm, as compared with data abstracted from the institutional charting system. The study included a total of 270 patients. According to the data from the chart audit, 55% of patients experienced at least 1 major surgical complication. Overall sensitivity, specificity, PPV, NPV and accuracy for the composite outcome was 72%, 80%, 82%, 70% and 76%, respectively. Diagnostic performance was poor for several of the individual complications. Our results showed that administrative data holdings can be used to capture a composite indicator of major surgical complications with adequate sensitivity and specificity. Additional work is required to identify suitable algorithms for several specific complications.

Validation of the recording of idiopathic pulmonary fibrosis in routinely collected electronic healthcare records in England

BackgroundRoutinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.MethodUsing the UK’s Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.ResultA total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3–65.3) for a “broad” codeset to 74.9% (95%CI:72.8–76.9) for a “narrow” codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4–81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.ConclusionHigh diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation.

EPH4 Validation of Novel Identification Algorithms for Nonfatal Myocardial Infarction Using Uniform Objective Criteria and Pooled Clinical Trial Data

Nonfatal myocardial infarction (MI) is primarily identified using diagnostic code-based algorithms in real-world data (RWD). This investigation aimed to utilize pooled clinical trial (CT) data to identify clinical indicators to optimize RWD MI identification.

Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.

This study aimed to develop and apply natural language processing (NLP) algorithms to identify recurrent atrial fibrillation (AF) episodes following rhythm control therapy initiation using electronic health records (EHRs). We included adults with new-onset AF who initiated rhythm control therapies (ablation, cardioversion, or antiarrhythmic medication) within two US integrated healthcare delivery systems. A code-based algorithm identified potential AF recurrence using diagnosis and procedure codes. An automated NLP algorithm was developed and validated to capture AF recurrence from electrocardiograms, cardiac monitor reports, and clinical notes. Compared with the reference standard cases confirmed by physicians' adjudication, the F-scores, sensitivity, and specificity were all above 0.90 for the NLP algorithms at both sites. We applied the NLP and code-based algorithms to patients with incident AF (n=22 970) during the 12 months after initiating rhythm control therapy. Applying the NLP algorithms, the percentages of patients with AF recurrence for sites 1 and 2 were 60.7% and 69.9% (ablation), 64.5% and 73.7% (cardioversion), and 49.6% and 55.5% (antiarrhythmic medication), respectively. In comparison, the percentages of patients with code-identified AF recurrence for sites 1 and 2 were 20.2% and 23.7% for ablation, 25.6% and 28.4% for cardioversion, and 20.0% and 27.5% for antiarrhythmic medication, respectively. When compared with a code-based approach alone, this study's high-performing automated NLP method identified significantly more patients with recurrent AF. The NLP algorithms could enable efficient evaluation of treatment effectiveness of AF therapies in large populations and help develop tailored interventions.

Measurement of clinical delay intervals among younger adults with colorectal cancer using health administrative data: a population-based analysis

BackgroundClinical delays may be important contributors to outcomes among younger adults (<50 years) with colorectal cancer (CRC). We aimed to describe delay intervals for younger adults with CRC using health...

Development and performance characteristics of novel code-based algorithms to identify invasive Escherichia coli disease.

Evaluation of novel code-based algorithms to identify invasive Escherichia coli disease (IED) among patients in healthcare databases. Inpatient visits with microbiological evidence of invasive bacterial disease were extracted from the Optum© electronic health record database between January 1, 2016 and June 30, 2020. Six algorithms, derived from diagnosis and drug exposure codes associated to infectious diseases and Escherichia coli, were developed to identify IED. The performance characteristics of algorithms were assessed using a reference standard derived from microbiology data. Among 97 194 eligible records, 25 310 (26.0%) were classified as IED. Algorithm 1 (diagnosis code for infectious invasive disease due to E. coli) had the highest positive predictive value (PPV; 96.0%) and lowest sensitivity (60.4%). Algorithm 2, which additionally included patients with diagnosis codes for infectious invasive disease due to an unspecified organism, had the highest sensitivity (95.5%) and lowest PPV (27.8%). Algorithm 4, which required patients with a diagnosis code for infectious invasive disease due to unspecified organism to have no diagnosis code for non-E. coli infections, achieved the most balanced performance characteristics (PPV, 93.6%; sensitivity, 78.1%; F1 score, 85.1%). Finally, adding exposure to antibiotics in the treatment of E. coli had limited impact on performance algorithms 5 and 6. Algorithm 4, which achieved the most balanced performance characteristics, offers a useful tool to identify patients with IED and assess the burden of IED in healthcare databases.

Performance characteristics of code‐based algorithms to identify urinary tract infections in large United States administrative claims databases

In real-world evidence research, reliability of coding in healthcare databases dictates the accuracy of code-based algorithms in identifying conditions such as urinary tract infection (UTI). This study evaluates the performance characteristics of code-based algorithms to identify UTI. Retrospective observational study of adults contained within three large U.S. administrative claims databases on or after January 1, 2010. A targeted literature review was performed to inform the development of 10 code-based algorithms to identify UTIs consisting of combinations of diagnosis codes, antibiotic exposure for the treatment of UTIs, and/or ordering of a urinalysis or urine culture. For each database, a probabilistic gold standard was developed using PheValuator. The performance characteristics of each code-based algorithm were assessed compared with the probabilistic gold standard. A total of 2950 641, 1831 405, and 2294 929 patients meeting study criteria were identified in each database. Overall, the code-based algorithm requiring a primary UTI diagnosis code achieved the highest positive predictive values (PPV; >93.8%) but the lowest sensitivities (<12.9%). Algorithms requiring three UTI diagnosis codes achieved similar PPV (>0.899%) and improved sensitivity (<41.6%). Algorithms requiring a single UTI diagnosis code in any position achieved the highest sensitivities (>72.1%) alongside a slight reduction in PPVs (<78.3%). All-time prevalence estimates of UTI ranged from 21.6% to 48.6%. Based on these findings, we recommend use of algorithms requiring a single UTI diagnosis code, which achieved high sensitivity and PPV. In studies where PPV is critical, we recommend code-based algorithms requiring three UTI diagnosis codes rather than a single primary UTI diagnosis code.

Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.

Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis. We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C). The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%). Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.

Validation of coding algorithms for the identification of herpes zoster among children.

To examine the validity of International Classification of Diseases, 10th Revision, (ICD-10) code-based algorithms for herpes zoster (HZ) in the electronic medical record (EMR) of a large, integrated pediatric healthcare network and to examine baseline demographics and chronic comorbidities associated with HZ in a representative pediatric population. We reviewed the electronic charts of all patients with a single ICD-10 for HZ (B02.xx) as their primary or secondary diagnosis in the EMR of the Children's Hospital of Philadelphia (CHOP) healthcare network from January 2010-March 2019. The positive predictive value (PPV) for a single code for HZ was calculated and alternative algorithms were examined to determine which method resulted in the highest PPV. The PPV for a single ICD-10 code was 91.7% (95% CI 80.8-95.4) for definitive and/or probable cases of HZ and 63.9% (95% CI 53.4%-75.5%) for definitive cases alone. Adding a prescription for an antiviral did not improve the PPV. However, adding a new code for rash entered within 1week of the HZ code increased the PPV to 100% for definitive and/or probable cases but with substantial loss of sensitivity. A high proportion of children with HZ who required inpatient hospitalization had chronic disease (70%) and were on systemic immunomodulatory therapy (50%). HZ can be identified with a high PPV in electronic medical records of children using ICD-10 code alone. These findings lay the foundation for future pharmacoepidemiologic research to better understand risk factors for HZ infection.

A New Code-Based Blind Signature Scheme

Abstract Blind signature is an important cryptographic primitive with widespread applications in secure e-commerce, for example to guarantee participants’ anonymity. Existing blind signature schemes are mostly based on number-theoretic hard problems, which have been shown to be solvable with quantum computers. The National Institute of Standards and Technology (NIST) began in 2017 to specify a new standard for digital signatures by selecting one or more additional signature algorithms, designed to be secure against attacks carried out using quantum computers. However, none of the third-round candidate algorithms are code-based, despite the potential of code-based signature algorithms in resisting quantum computing attacks. In this paper, we construct a new code-based blind signature (CBBS) scheme as an alternative to traditional number-theoretic based schemes. Specifically, we first extend Santoso and Yamaguchi’s three pass identification scheme to a concatenated version (abbreviated as the CSY scheme). Then, we construct our CBBS scheme from the CSY scheme. The security of our CBBS scheme relies on hardness of the syndrome decoding problem in coding theory, which has been shown to be NP-complete and secure against quantum attacks. Unlike Blazy et al.’s CBBS scheme which is based on a zero-knowledge protocol with cheating probability $2/3$, our CBBS scheme is based on a zero-knowledge protocol with cheating probability $1/2$. The lower cheating probability would reduce the interaction rounds under the same security level and thus leads to a higher efficiency. For example, to achieve security level $2^{-82}$, the signature size in our CBBS scheme is $1.63$ MB compared to $3.1$ MB in Blazy et al.’s scheme.