Review for 10.1186/s12876-025-03953-5 (2025)

Title: Care needs profiles of Crohn's disease patients and their associations with symptom
clusters, post-traumatic growth, and family function: A latent profile analysis
Version: 0 Date: 04 Apr 2025
Reviewer's report:
Major Comments
1. Sample Representativeness and Selection Bias
- The study relies on a **convenience sample** from a single tertiary hospital in Nanjing, China.
Patients in tertiary settings often present with more severe/complex disease states, potentially
skewing results toward higher care needs and symptom severity. This limits generalizability
to broader Crohn’s disease (CD) populations, including those in primary care, rural areas, or
regions with differing healthcare infrastructures.
2.  The **cross-sectional design** precludes causal interpretations. For example, while family
function and post-traumatic growth (PTG) correlate with care needs, bidirectional relationships
(e.g., poor family function exacerbating needs or unmet needs worsening family dynamics)
cannot be ruled out. Longitudinal data are critical to understanding temporal dynamics.
3.  The **Crohn’s Disease Care Needs Scale (CD-CNS)** was developed and validated within
the authors’ team and tested only in a Chinese clinical cohort. Its psychometric properties
(e.g., construct validity, cross-cultural applicability) remain unverified in diverse populations.
External validation is necessary to confirm its reliability and generalizability.
4. Latent Profile Analysis (LPA) Model Justification
- While fit indices (AIC, BIC, entropy) support the three-class model, the rationale for selecting
this model over alternatives (e.g., four-class) is inadequately explained. Critical metrics like the
**Bootstrap Likelihood Ratio Test (BLRT)** and **Lo-Mendell-Rubin (LMR)** results are
mentioned but not contextualized, weakening confidence in the optimality of the three-class
solution.
5. Unmeasured Confounders
- Key variables influencing care needs (e.g., **specific treatments** [biologic
vs. immunomodulator therapy], **comorbidities**, or **biomarkers** like CRP/fecal
calprotectin) were not included. These omissions may bias observed associations between
symptom burden, disease activity, and care needs.
6. Statistical Multiple Testing
- The use of **multiple logistic regression models** without adjustment for multiple
comparisons (e.g., Bonferroni correction) increases the risk of **Type I errors**. Reported
associations (e.g., stoma presence, PTG) may overstate significance due to inflated false-positive
rates.
Minor Comments
1. Table 1: The "Current diet" categories list frequencies (e.g., "Normal diet" n=95, "Restrictive
diet" n=26) that do not sum to the total sample size (n=250), suggesting formatting or reporting
errors.
- Table 3: Percentages for some variables (e.g., "Current diet") do not align with the total sample
size (e.g., Class 1 n=96 but percentages sum inconsistently).
2. Terminological Ambiguities
- Terms like **"sickness expenditure per year"** and **"annual expenditure on illness"** are
used interchangeably, creating confusion. Standardized terminology would improve clarity.
3. Missing Figure Reference
- Figure 1 ("characteristic distribution of 3 potential categories") is referenced but not provided,
limiting interpretability of the LPA results.
4. Reference Formatting Issues
- **Reference 33** ("Healthy China 2030 Planning Outline") is cited as a URL without archival
details (e.g., DOI, publication date).
- **Citation ^\[50\]^** in the Limitations section refers to biomarkers but is absent from the
reference list.
Limitations
1. Single-Center Recruitment
- The study population is restricted to a **tertiary hospital** in Nanjing, excluding patients from
primary care or rural settings who may exhibit distinct care needs.
2. Self-Report Bias
- Data collection relied on **self-reported questionnaires**, which are vulnerable to recall bias
(e.g., symptom severity) and social desirability bias (e.g., underreporting psychological needs).
3. Exclusion of Comorbid Populations
- Patients with **mental illness** or **severe comorbidities** (e.g., organ insufficiency) were
excluded, potentially omitting individuals with heightened care needs and skewing profiles
toward less complex cases.
4. Cross-Sectional Temporal Limitations
- The design cannot capture **fluctuations in care needs** over time (e.g., during disease flares
vs. remission), which are critical for understanding dynamic patient trajectories.
5. Cultural Specificity
- Findings are rooted in **Chinese cultural norms** (e.g., family roles, healthcare access),
limiting applicability to populations with differing sociocultural contexts (e.g., Western
settings).
6. Lack of Objective Biomarkers
- Reliance on **patient-reported symptoms** and the Harvey-Bradshaw Index (HBI) overlooks
**objective measures** (e.g., endoscopic scores, CRP), reducing clinical precision in linking
disease activity to care needs.