Predicting Day-Case Arthroplasty Outcomes

Which patient factors best predict success/failure of day-case arthroplasty?

Increased age, female sex (OR 1.63-4.22), high ASA classification, and multiple comorbidities (OR up to 3.9) are the primary predictors of day-case arthroplasty failure.

Abstract

Day‐case arthroplasty outcomes are influenced by clear patient factors. Studies report that increased age—which in over 20 analyses shows odds ratios from 1.01 to 4.16—and female sex—with odds ratios from 1.63 to 4.22 in more than 15 investigations—predict lower same‐day discharge success. A higher body mass index (supported in 10+ studies) and non‐white race (noted in 5+ studies) similarly relate to reduced success. In addition, clinical indicators such as an ASA classification of III or IV and a comorbidity burden of three or more conditions consistently predict failure, with odds ratios up to 3.9 and highly significant p‐values. Other conditions, including hypertension, diabetes, cardiovascular disease, and chronic obstructive pulmonary disease, are also cited as contributing to poor day‐case outcomes. Although a couple of studies flag younger age (<40) as a potential risk factor, this finding remains inconsistent.

Methods

We analyzed 40 sources from an initial pool of 500, using 8 screening criteria. Each paper was reviewed for 6 key aspects that mattered most to the research question. More on methods

Papers identified with Elicit search

n = 500

Papers screened using: Study Setting, Patient Population - Age, Procedure Type, Clinical Context, Predictive Factors, Outcome Measures, Study Design, Study Focus

n = 500

Papers screened out

n = 460

Papers included for extraction

n = 40

Paper search

Using your research question “Which patient factors best predict success/failure of day-case arthroplasty?”, we searched across over 126 million academic papers from the Semantic Scholar corpus. We retrieved the 500 papers most relevant to the query.

Screening

We screened in sources based on their abstracts that met these criteria:

• Study Setting: Does the study investigate outpatient/day-case/ambulatory arthroplasty procedures?
• Patient Population - Age: Does the study include only adult patients (≥18 years)?
• Procedure Type: Does the study examine primary (non-revision) hip, knee, shoulder, or ankle arthroplasty?
• Clinical Context: Does the study examine planned elective procedures (not emergency or trauma-related cases)?
• Predictive Factors: Does the study analyze patient-related predictive factors (such as age, BMI, comorbidities, or social support)?
• Outcome Measures: Does the study report specific outcomes related to success/failure of day-case surgery (such as same-day discharge, unplanned overnight stays, or 30-day readmissions)?
• Study Design: Is the study design either a prospective/retrospective cohort study, case-control study, RCT, or systematic review with 10 or more patients?
• Study Focus: Does the study include patient-related factors (not exclusively focusing on surgical technique, anesthetic protocol, or hospital/surgeon factors)?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

Study Design:

• Identify the specific type of study design used:
  • Specify whether it is retrospective, prospective, or cross-sectional
  • Note if it is a single-center or multi-center study
• Look in the methods section for explicit statements about study design. If the design is not clearly stated, carefully review the methodology description to determine the most appropriate classification.

Patient Selection Criteria:

• Extract the specific inclusion and exclusion criteria used for patient selection:
  • List all explicit inclusion criteria (e.g., age range, type of arthroplasty, patient health status)
  • List all explicit exclusion criteria
  • Note any specific screening or eligibility assessment methods

Patient Demographic and Clinical Factors:

• Extract key patient characteristics that could predict day-case arthroplasty success:
  • Age (mean and range)
  • Gender distribution
  • Body Mass Index (if reported)
  • Comorbidities (e.g., Charnley class, ASA score)
  • Previous joint surgeries
  • Preoperative medication use

Reasons for Same-Day Discharge Failure:

• List all documented reasons for unsuccessful same-day discharge:
  • Rank reasons by frequency if provided
  • Extract specific percentages or proportions for each reason

Predictive Factors for Same-Day Discharge Success:

• Extract statistical predictors of same-day discharge success:
  • List all statistically significant factors
  • Record odds ratios, confidence intervals, and p-values

Surgical Procedure Details:

• Extract specific details about surgical procedures:
  • Types of arthroplasty performed
  • Surgical timing (e.g., surgery start time)
  • Anesthesia type
  • Surgical duration

Results

Characteristics of Included Studies

Study Design:

• 30 retrospective studies
• 4 prospective studies
• 5 systematic reviews (including meta-analyses)
• 1 review

Setting Type:

• 17 studies in single-center settings
• 10 studies in multi-center settings
• No mention found for 9 studies
• 5 reviews or systematic reviews without a setting

Sample Size:

• 1 study with fewer than 100 participants
• 10 studies with 100–500 participants
• 4 studies with 500–1,000 participants
• 15 studies with 1,000–10,000 participants
• 7 studies with more than 10,000 participants

Primary Outcome Category:

• 25 studies focused on same-day discharge (SDD) or day-case success/failure
• 11 studies focused on complications, readmission, or reoperation
• 17 studies focused on predictors or risk factors (for same-day discharge, length of stay, etc.)
• 7 studies focused on other outcomes such as patient selection, safety, or general outcomes

Patient Demographics

Clinical Characteristics

Perioperative Factors

Success Rates and Risk Patterns

Overall Success Rates

Common Failure Patterns

Risk Stratification Models

References

1. James Murrell et al. (2023)
2. M. Keulen et al. (2020)
3. David P Foley et al. (2023)
4. A. Sher et al. (2017)
5. Oddrún Danielsen et al. (2024)