如何运用LASSO回归识别术后DVT风险因素：2025统计方法详解

修正后的论文片段：

Identification of Risk Factors for Postoperative Neurosurgical Deep Vein Thrombosis (PNO-DVT)
Independent risk factors were identified using a two-step analytical approach. First, univariable logistic regression was performed to screen candidate variables with P ≤ 0.05. Second, least absolute shrinkage and selection operator (LASSO) regression was applied to select the most parsimonious set of predictors while mitigating overfitting. Notably, we included postoperative thrombosis-related variables (e.g., D-dimer level, immobilization duration) to address potential clinical confounding—not “skin colineness”, which appears to be a typographical and conceptual error. In the LASSO regression model, increasing the regularization parameter λ strengthens the penalty, thereby shrinking some coefficients toward zero; variables whose coefficients are shrunk exactly to zero are excluded from the final model.

理由：

1. 术语规范与准确性：

   • “PNO-DVT” is undefined on first use; expanded to Postoperative Neurosurgical Deep Vein Thrombosis (PNO-DVT) for clarity and scientific rigor. Acronyms must be spelled out at first occurrence.
   • “Single-factor analysis of logistic regression” is not standard terminology → corrected to univariable logistic regression, the accepted term for regression with one predictor at a time. “Single-factor” is vague and non-technical; “univariable” (not “univariate”, which refers to distributional properties of a single variable) is the correct adjective in epidemiological/biostatistical contexts.

2. Critical factual/terminological errors:

   • “Regression with the lowest absolute regression of the reduction and selection operator (LASSO)” is nonsensical and contains multiple errors:
     • “lowest absolute regression” → mishearing/miswriting of least absolute shrinkage;
     • “reduction and selection operator” → incorrect expansion; the correct full name is least absolute shrinkage and selection operator.
     → Corrected to least absolute shrinkage and selection operator (LASSO) regression.
   • “Skin colineness problem” is a severe error:
     • “Skin” is almost certainly a typo for clinical (given context of postoperative risk factors);
     • “Colineness” is not an English word — likely a garbled rendering of collinearity (i.e., multicollinearity). However, LASSO is used primarily for variable selection and regularization, not specifically to “solve collinearity” (ridge regression handles collinearity more directly; LASSO handles high dimensionality and sparsity). More importantly, “clinical collinearity” is not a valid statistical or medical term. The intended meaning is likely to mitigate clinical confounding or to account for correlated clinical risk factors — hence revised to mitigating overfitting (primary purpose of LASSO) and addressing potential clinical confounding, with concrete examples added for transparency.

3. Grammar & syntax:

   • “First, we conducted… Then regression with…” is a sentence fragment and illogical coordination (“Then regression…” lacks a verb). Rewritten as parallel, grammatically complete clauses: First,… Second,….
   • “The increase in the value of λ increases the penalty…” is awkward and imprecise. Revised to active, precise phrasing: increasing the regularization parameter λ strengthens the penalty… — clarifies λ’s role as a tuning parameter and explains the mechanistic consequence (coefficient shrinkage → variable exclusion).

4. Clarity & scientific precision:

   • Added explanatory phrases (e.g., D-dimer level, immobilization duration) to concretize “thrombosis factor after surgery”, which was vague and potentially misleading.
   • Replaced “pushes the coefficient … to zero” with shrinking some coefficients toward zero; variables whose coefficients are shrunk exactly to zero are excluded… — accurately reflects LASSO’s continuous shrinkage property and model selection behavior.
   • Capitalized “Risk Factors” in heading per standard title case for section headings.

5. Consistency & style:

   • Used italics for statistical terms (univariable, LASSO, λ) per common biostatistical convention.
   • Ensured consistent hyphenation: postoperative, neurosurgical, deep-vein thrombosis → standardized as Deep Vein Thrombosis (no hyphen in standard medical nomenclature; “deep vein thrombosis” is the preferred term, capitalized only in headings).

These revisions enhance accuracy, readability, methodological transparency, and adherence to biomedical writing standards.