**1. Introduction**

Dry eye is a common postoperative complaint from patients who underwent manual cataract surgery (MCS) with conventional phacoemulsification [1,2]. Symptoms include foreign body sensation, pain, blurred vision, ocular discomfort, burning, and dryness. These symptoms negatively affect patients' satisfaction with surgery, quality of life, and burden public health [3]. After cataract surgery, signs of dry eye include a decreased tear breakup time, decreased corneal sensitivity, and increased ocular surface staining [2,4,5]. The pathogenic factors consist of inflammation, microscopic damage, neurosensory destruction on the ocular surface, tear film instability, and hyperosmolarity [6–8].

Since 2010, the femtosecond laser has been used in cataract surgery. Femtosecond laser-assisted cataract surgery (FLACS) provides precise anterior capsulotomy, safe lens fragmentation, and accurate corneal incision. Thus, it uses less ultrasound energy and phacoemulsification time [9], possibly leading to less postoperative inflammation and less dry eye. However, direct contact of the ocular surface with the vacuum and sustained pressure of the suction ring during FLACS may cause hyperaemia and microscopic damage

**Citation:** Chen, W.-T.; Chen, Y.-Y.; Hung, M.-C. Dry Eye Following Femtosecond Laser-Assisted Cataract Surgery: A Meta-Analysis. *J. Clin. Med.* **2022**, *11*, 6228. https:// doi.org/10.3390/jcm11216228

Academic Editor: Nobuyuki Shoji

Received: 9 September 2022 Accepted: 21 October 2022 Published: 22 October 2022

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to the ocular surface. In addition, laser procedures in FLACS may potentially affect the tear film [10]. All these reasons may result in dry eye.

Previous studies comparing FLACS and MCS were primarily concerned with the refractory outcome (e.g., visual acuity and spherical equivalent) and complication rate (e.g., anterior capsule tear or posterior capsule rupture) [9,11–16]. However, very few studies have investigated post-FLACS dry eye or compared the risk of dry eye between the two surgery groups. Therefore, we conducted this meta-analysis to investigate the impact of FLACS on dry eye and then compared postoperative dry eye after FLACS and MCS.

#### **2. Materials and Methods**

#### *2.1. Search Strategy*

This study was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We searched the PubMed, EMBASE, and Cochrane databases for studies published from 1 January 2000 to 15 October 2022, using the keywords 'femtosecond laser-assisted cataract surgery' and 'dry eye'. Studies were screened first by examining the titles and abstracts and then scrutinising full texts. Bibliographies were also manually searched for the relevant literature.

#### *2.2. Inclusion and Exclusion Criteria*

Only peer-reviewed journal articles were included. They should be original, prospective, or randomised control clinical studies investigating dry eye presentation after FLACS. Reviews, meta-analyses, or conference abstracts were excluded because of repeated data. Two researchers (W.-C. Chen and Y.-Y. Chen) independently assessed the articles. A third researcher (M.-C. Hung) intervened if consensus was not reached.

Evaluation of the quality of included articles was performed independently by two researchers (W.-C. Chen and Y.-Y. Chen) using ROBINS-I risk of bias assessment tool. A third researcher (M.-C. Hung) reassessed and made the final decision if discrepancies occurred. ROBINS-I assesses the risk of bias in 7 domains, including confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported result. Each domain contains a set of questions (criteria). The risk of bias judgement of each domain was categorised into 'Low risk', 'Moderate risk', 'Serious risk', and 'Critical risk' of bias. Then, the overall risk of bias was judged according to the assessment of each domain.

#### *2.3. Data Extraction*

The following data were tracked from each included article: the first author, year of publication, and number/age/gender of participants. We also recorded the baseline (preoperative) and postoperative parameters regarding dry eye with: the ocular surface disease index (OSDI), tear meniscus height, Schirmer's test, fluorescein staining, first tear breakup time, and average tear breakup time.

#### *2.4. Definitions of Parameters*

The OSDI was adopted to evaluate dry eye symptoms. The questionnaire included 12 questions about eye discomfort, visual function, and environmental triggers. A higher OSDI implies more severe dry eye [17]. Tear meniscus height was assessed via corneal topography in order to measure the height of the inferior tear meniscus [18]. A lower tear meniscus height implies a sign of dry eye. Schirmer's test, also an index of tear secretion, was performed with sterile strips inserted at the lateral third of the lower eyelid margin [19]. The strips were removed five minutes later and the amount of wetting of the paper strips was measured. A lower Schirmer score suggests the diagnosis of dry eye. Fluorescein staining was applied to assess ocular surface damage [20]. Topical fluorescein readily enters and stains the corneal stroma where the epithelium is absent or when the epithelial cells have lost intercellular junctions. A higher score of fluorescein staining is a sign of dry eye. Tear film breakup time is a clinical evaluation of evaporative dry eye disease. Further, it

is performed by instilling topical fluorescein into the eyes [21]. The number of seconds that elapsed between the last blink and the appearance of the first dry spot in the tear film was recorded as the first tear breakup time. Similarly, the average tear breakup time was recorded. A higher tear breakup time indicates tear film instability.

#### *2.5. Statistical Analysis*

Meta-analysis was performed using the Comprehensive Meta-Analysis software, version 3 (Biostat, Englewood, NJ, USA). First, we calculated the standardised mean differences (SMDs) of each index between the post-FLACS time points and baseline. The SMD from each study was computed by dividing the mean difference between each time point and baseline by the standard deviation in order to ensure that the difference was on the same scale. Then, the SMDs were pooled to derive the overall differences between post-FLACS and baseline according to each time point. Second, we compared the differences between the FLACS and MCS groups. The SMDs from each study were pooled to derive the overall values using a similar algorithm. Thus, we could then know which surgery was favoured. The heterogeneity among the studies was determined using the *I* 2 statistic, and an *I* 2 statistic of ≥50% would represent high heterogeneity. Funnel plots and Egger's test were used to assess publication bias.
