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Abstract Background A retrospective observational study was conducted at 3 health care organizations to identify clinical gaps in care for patients with stage 3 or 4 chronic kidney disease (CKD), and financial opportunity from U.S. risk adjustment payment systems. Lack of evaluation for CKD in patients with diabetes was also assessed. Methods Outpatient longitudinal laboratory results and patient metadata available in the electronic medical record, laboratory information system, and/or laboratory billing or facility claims data for the calendar year 2021 were evaluated. Laboratory results were compared to billing data (ICD-10 codes) and risk adjustment scores including Hierarchical Condition Categories (HCC) to determine if laboratory-identified CKD was coded as a disease condition in the electronic medical record. Adults 18 to 75 years of age were included; inpatient laboratory results and pregnant individuals were excluded. Results At the 3 institutions, 12,478 of 16,063 (78%), 487 of 1511 (32%) and 19,433 of 29,277 (66%) of patients with laboratory evidence of stage 3 or 4 CKD did not have a corresponding ICD-10 or HCC code for CKD in the electronic medical record. For patients at the 3 institutions with diabetes on the basis of an HbA1c value of ≥ 6.5%, 34,384 of 58,278 (59%), 2274 of 2740 (83%) and 40,378 of 52,440 (77%) had not undergone guideline-recommended laboratory testing for CKD during the same 12 months. Using publicly available data for calendar year 2021, an estimated 3246 of 32,398 patients (9.9%) at the 3 institutions with undocumented CKD stages 3–4 would be enrolled in Medicare Advantage or Affordable Care Act Marketplace programs. The imputed lost reimbursement under risk-adjusted payment systems for under-documentation of CKD in this subset of patients was $2.85 M for the three institutions combined, representing lost opportunity for both identification and proactive clinical management of these patients, and financial recovery for the costs of providing that care. Conclusions Clinical laboratories can provide value beyond routine diagnostics, helping to close gaps in care for identification and management of CKD, stratifying subgroups of patients to identify risk, and capturing missed reimbursement through risk adjustment factors.

Objective: To obtain complete DNA sequences of adenoviral (AdV) D8 genome from patients with conjunctivitis and determine the relation of sequence variation to clinical outcomes. Design: This study is a post hoc analysis of banked conjunctival swab samples from the BAYnovation Study, a previously conducted, randomized controlled clinical trial for AdV conjunctivitis. Participants: Ninety-six patients with AdV D8-positive conjunctivitis who received placebo treatment in the BAYnovation Study were included in the study. Methods: DNA from conjunctival swabs was purified and subjected to whole-genome viral DNA sequencing. Adenovirus D8 variants were identified and correlated with clinical outcomes, including 2 machine learning methods. Main Outcome Measures: Viral DNA sequence and development of subepithelial infiltrates (SEIs) were the main outcome measures. Results: From initial sequencing of 80 AdV D8-positive samples, full adenoviral genome reconstructions were obtained for 71. A total of 630 single-nucleotide variants were identified, including 156 missense mutations. Sequence clustering revealed 3 previously unappreciated viral clades within the AdV D8 type. The likelihood of SEI development differed significantly between clades, ranging from 83% for Clade 1 to 46% for Clade 3. Genome-wide analysis of viral single-nucleotide polymorphisms failed to identify single-gene determinants of outcome. Two machine learning models were independently trained to predict clinical outcome using polymorphic sequences. Both machine learning models correctly predicted development of SEI outcomes in a newly sequenced validation set of 16 cases (P = 1.5 × 10−5). Prediction was dependent on ensemble groups of polymorphisms across multiple genes. Conclusions: Adenovirus D8 has ≥ 3 prevalent molecular substrains, which differ in propensity to result in SEIs. Development of SEIs can be accurately predicted from knowledge of full viral sequence. These results suggest that development of SEIs in AdV D8 conjunctivitis is largely attributable to pathologic viral sequence variants within the D8 type and establishes machine learning paradigms as a powerful technique for understanding viral pathogenicity.