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Background: Vertebral osteomyelitis and discitis (VOD), an infection of intervertebral discs, often requires spine surgical intervention and timely management to prevent adverse outcomes. Our study aims to develop a machine learning (ML) model to predict the indication for surgical intervention (during the same hospital stay) versus nonsurgical management in patients with VOD. Methods: This retrospective study included adult patients (≥18 years) with VOD (ICD-10 diagnosis codes M46.2,3,4,5) treated at a single institution between 01/01/2015 and 12/31/2019. The primary outcome studied was surgery. Candidate predictors were age, sex, race, Elixhauser comorbidity index, first-recorded lab values, first-recorded vital signs, and admit diagnosis. After splitting the dataset, XGBoost, logistic regression, and K-neighbor classifier algorithms were trained and tested for model development. Results: A total of 1,111 patients were included in this study, among which 30% (n=339) of patients underwent surgical intervention. Age and sex did not significantly differ between the two groups; however, race did significantly differ (P<0.0001), with the surgical group having a higher percentage of white patients. The top ten model features for the best-performing model (XGBoost) were as follows (in descending order of importance): admit diagnosis of fever, negative culture, Staphylococcus aureus culture, partial pressure of arterial oxygen to fractional inspired oxygen ratio (PaO2:FiO2), admit diagnosis of intraspinal abscess and granuloma, admit diagnosis of sepsis, race, troponin I, acid-fast bacillus culture, and alveolar-arterial gradient (A-a gradient). XGBoost model metrics were as follows: accuracy =0.7534, sensitivity =0.7436, specificity =0.7586, and area under the curve (AUC) =0.8210. Conclusions: The XGBoost model reliably predicts the indication for surgical intervention based on several readily available patient demographic information and clinical features. The interpretability of a supervised ML model provides robust insight into patient outcomes. Furthermore, it paves the way for the development of an efficient hospital resource allocation instrument, designed to guide clinical suggestions.
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Importance: Metformin is often used as a first-line therapy for type 2 diabetes; however, frequent discontinuation with reduced kidney function and increased disease severity indicates that a comparison with any other group (eg, nonusers or insulin users) must address significant residual confounding concerns. Objectives: To examine the potential for residual confounding in a commonly used observational study design applied to metformin and to propose a more robust study design for future observational studies of metformin. Design, Setting, and Participants: This retrospective cohort study with a prevalent user design was conducted using an administrative claims database for Medicare Advantage beneficiaries in the US. Participants were categorized into 2 distinct cohorts: 404â¯458 individuals with type 2 diabetes and 81â¯791 individuals with prediabetes. Clinical history was observed in 2018, and end points were observed in 2019. Statistical analyses were conducted between May and December 2021. Exposures: Prevalent use (recent prescription and history of use on at least 90 of the preceding 365 days) of metformin or insulin but not both at the start of the observation period. Main Outcomes and Measures: Total inpatient admission days in 2019 and total medical spending (excluding prescription drugs) in 2019. Each of these measures was treated as a binary outcome (0 vs >0 inpatient days and top 10% vs bottom 90% of medical spending). Results: The study included 404â¯458 adults with type 2 diabetes (mean [SD] age, 74.5 [7.5] years; 52.7% female). A strong metformin effect estimate was associated with reduced inpatient admissions (odds ratio, 0.60; 95% CI, 0.58-0.62) and reduced medical expenditures (odds ratio, 0.57; 95% CI, 0.55-0.60). However, implementation of additional robust design features (negative control outcomes and a complementary cohort) revealed that the estimated beneficial effect was attributable to residual confounding associated with individuals' overall health, not metformin itself. Conclusions and Relevance: These findings suggest that common observational study designs for studies of metformin in a type 2 diabetes population are at risk for consequential residual confounding. By performing 2 additional validation checks, the study design proposed here exposes residual confounding that nullifies the initially favorable claim derived from a common study design.
