Predictors of time to recovery from cataract surgery among cataract patients at Menelik II Comprehensive Specialized Hospital: a retrospective follow up study

Abstract

Background

Cataracts are the leading cause of reversible blindness globally, disproportionately affecting populations in low- and middle-income countries. In Ethiopia, cataracts remain a significant public health concern. Despite the effectiveness of cataract surgery in restoring vision, information on recovery time and its predictors remain limited. The aim of this study was to assess time to recovery and its predictors among patients undergoing cataract.

Methods

A retrospective cohort study was conducted on 459 cataract patients who underwent surgery between January 1 and December 31, 2023. Data were randomly extracted from their medical records between June 1 and August 15, 2024. The Kaplan-Meier method was used to estimate the survival probabilities and compare groups, with significant differences tested using the log-rank test. The Weibull regression with the inverse Gaussian frailty was applied following a goodness-of-fit test to identify predictors of time to recovery. Results are presented as adjusted hazard ratios (AHRs) with 95% confidence intervals (CIs). All statistical tests were declared significant at P-value < 0.05.

Results

we reviewed 459 patient cards, and 368 (80.17%, 95% CI: 76.26–83.58%) had recovered from a cataract surgery over 7,919.28 person-weeks. The overall incidence rate of recovery was at 46.47 per 1,000 persons per week (95% CI: 41.95–51.47). The median recovery time was at 18.14 weeks (IQR: 12.29–24, 95% CI: 17.14–18.86). Age over 60 years (AHR = 0.25, 95% CI: 0.07–0.96), urban residence (AHR = 1.77, 95% CI: 1.15–2.70), preoperative visual acuity (medium: AHR = 1.98, 95% CI: 1.23–3.18; high: AHR = 5.83, 95% CI: 1.72–19.77), comorbidities (ocular: AHR = 0.30, 95% CI: 0.15–0.61; systemic: AHR = 0.41, 95% CI: 0.22–0.75), type of surgery (phacoemulsification: AHR = 1.98, 95% CI: 1.06–3.67; intracapsular cataract extraction: AHR = 0.14, 95% CI: 0.03–0.89), and complications (intraoperative: AHR = 0.29, 95% CI: 0.12–0.71; postoperative: AHR = 0.17, 95% CI: 0.06–0.47), and surgeries performed by an ophthalmologist (AHR = 3.44, 95% CI: 1.80–6.55) were statistically significant predictors of time to recovery from cataract surgery.

Conclusion

The median recovery time was shorter than in previous local studies but longer than in developed countries. Improved preoperative assessment, better comorbidity management, and minimizing complications may contribute to better recovery outcomes. The use of phacoemulsification and procedures performed by experienced ophthalmologists were associated with shorter recovery times, but further prospective studies are needed to confirm these findings. Personalized care approaches are recommended to optimize postoperative recovery.

Introduction

Cataracts are a leading cause of visual impairment and blindness worldwide, disproportionately affecting low- and middle-income countries, including Ethiopia [1,2,3]. Globally, approximately 94 million people suffer from distance vision impairment or blindness due to cataracts [1]. Nearly 90% of global blindness cases occur in low-income countries, with cataracts accounting for 50% of these cases, making it a significant yet avoidable cause of blindness [1, 3]. Despite global efforts to reduce cataract-related blindness, disparities in access to timely treatment persist in many low-resource settings [4, 5]. In Ethiopia, cataracts contribute to 42.3% of low vision cases and 49.9% of blindness cases, adding to the national blindness rate of 1.6% and impaired vision prevalence of 3.7%. More than 80% of these cases are potentially preventable or treatable [3].

Beyond visual impairment, untreated cataracts impose a significant socioeconomic burden. They increase the risk of dementia [6], falls, and road accidents in older adults [7, 8], reduce quality of life, and contribute to increased mortality [9, 10]. Cataracts also impact productivity and independence, leading to broader economic consequences for individuals, families and communities [11]. Globally, an estimated 30% of disability-adjusted life years (DALYs) lost due to blindness and vision problems is attributed to cataracts [12].

Currently, there are no approved medications to prevent or reverse cataracts; making surgery is the only effective treatment [13,14,15,16]. Each year, approximately 9.5 million cataract surgeries are performed worldwide, significantly reducing the burden of vision impairment [15]. However, access to cataract surgery remains a major challenge in many low- and middle-income countries due to shortages of ophthalmologists, high surgical costs, and an unequal distribution of eye care services [5, 17, 18]. In Ethiopia, these barriers contribute to delays in treatment, prolonged recovery times, and an increased risk of complications [19]. Efforts are underway to address these challenges by increasing the number of trained ophthalmologists and expanding eye care facilities to underserved areas [17].

Despite the effectiveness of cataract surgery in restoring vision, there is limited evidence regarding the time required for full recovery and the factors influencing postoperative healing in resource-limited settings [20]. Recovery time after cataract surgery varies widely across different healthcare settings. Studies from high-income countries report recovery times of 4–12 weeks [21, 22], with most patients regaining functional vision within a month due to advanced surgical techniques, better postoperative care, and early rehabilitation programs [23,24,25,26].

In contrast, data on recovery times in low-income countries, particularly in Sub-Saharan Africa and South Asia, remain scarce [20]. However, recovery periods in these regions are likely prolonged due to limited access to advanced surgical techniques, inadequate postoperative care, and a higher prevalence of comorbidities [5, 17, 18]. In Ethiopia, for instance, a study from the north reported a median recovery time of 23 weeks (IQR: 16–35 weeks) [27], highlighting the stark contrast with high-income settings. Similar challenges may exist in other low-resource settings, though specific recovery time estimates are limited. Additionally, the full range of factors influencing recovery time remains unclear, including marital status, intraocular lens (IOL) site, operated eye type, ocular comorbidities, intraoperative and postoperative complications, and surgeon expertise.

This gap in knowledge presents challenges for both patients and healthcare providers. Patients often face uncertainty and unrealistic expectations, complicating their postoperative journey and resulting in increased anxiety, disappointment, and frustration [28, 29]. Healthcare providers struggle with unclear recovery timelines, making it difficult to optimize postoperative care, follow-up scheduling, and patient assessment [30, 31]. Without robust local data, policymakers and clinicians in Ethiopia lack clear benchmarks for recovery expectations, leading to suboptimal patient management and resource allocation.

Understanding the predictors of recovery time is crucial for improving surgical outcomes, patient counseling, and follow-up care in Ethiopia and other resource-limited settings. Identifying modifiable predictors such as surgical technique, complication management, surgeon expertise, and comorbidity management helps guide targeted interventions to enhance recovery outcomes. Additionally, this study provides evidence that informs policy decisions aimed at improving cataract surgery practices and healthcare resource allocation in Ethiopia.

By addressing these critical gaps, this study quantifies recovery time and identifies key predictors influencing postoperative healing among cataract patients at Menelik II Comprehensive Specialized Hospital. The findings support patients in setting realistic recovery expectations, assist clinicians in tailoring patient management strategies, and contribute to refining national eye health programs. Ultimately, this research offers actionable insights to enhance surgical outcomes and follow-up care in resource-limited settings.

Materials and methods

Study area

The study was conducted at Menelik II Comprehensive Specialized Hospital, a public tertiary health facility located in Addis Ababa, the capital of Ethiopia. Established in 1910, the hospital is the country’s first and largest ophthalmology center. Managed by the Addis Ababa Health Bureau, it provides specialized eye care services to critically ill patients referred from across Ethiopia. The ophthalmology department is staffed by 22 specialists and 6 subspecialists and is equipped with 60 beds, 45 examination rooms, 4 operating theaters, and dedicated teaching facilities. The hospital offers a range of cataract treatments, including both standard and complex surgeries, and performs between 2,000 and 2,500 cataract surgeries annually on average.

Study design and period

A one-year facility-based retrospective cohort study was conducted at Menelik II Comprehensive Specialized Hospital. Secondary data were obtained from the electronic medical records system and medical charts of cataract patients treated with cataract surgery between January 1, 2023, and December 31, 2023, which served as the study period. Data were extracted from June 1, 2024, to August 15, 2024.

Population

Source population

All cataract patients were treated with cataract surgery at Menelik II Comprehensive Specialized Hospital.

Study population

All cataract patients were treated with cataract surgery at Menelik II Comprehensive Specialized Hospital from January 1, 2023, to December 31, 2023.

Inclusion and exclusion criteria

Inclusion criteria

The medical records of cataract patients treated with different types of cataract surgery at Menelik II Comprehensive Specialized Hospital between January 1, 2023, and December 31, 2023, were included in the study.

Exclusion criteria

Cataract patients who had undergone cataract surgery but whose medical records were incomplete were excluded from the study. Incomplete records were defined as missing data on surgery date, patient baseline data, or other important predictors or if the medical charts themselves were unavailable.

Sample size and sampling procedure

Sample size determination

To ensure adequate statistical power for analyzing recovery time from cataract surgery and its predictors, the sample size was calculated using the stpower log-rank test in Stata version 17. The estimation was based on significant predictors of recovery time identified in a previous study conducted in northern Ethiopia, including place of residence (AHR = 1.59), visual acuity (medium: AHR = 4.14; high: AHR = 5.23), type of cataract (traumatic: AHR = 1.75; secondary: AHR = 2.59), and type of surgical procedure (AHR = 1.43) [27]. Adjusted hazard ratios (AHR) were used instead of crude hazard ratios (CHR) to account for potential confounders and provide a more accurate effect estimate. Assumptions of 80% power, a 5% α error, and a 10% allowance for incomplete data were incorporated into the calculation. The sample sizes estimated for each predictor were compared, and the largest one was selected as the final minimum sample size. This was determined to be 494. Details of these calculations are summarized in Table 1.

Table 1 Sample size calculation for predictors of time to recovery from cataract surgery among cataract patients at menelik II comprehensive specialized hospital, 2024

Sampling procedures

The year 2023 was chosen for record review as it offered the most recent and comprehensive data on the problem under investigation. Medical record numbers (MRNs) of all cataract patients who underwent surgery between January 1, 2023, and December 31, 2023, were retrieved from the ophthalmic operation room registry and listed in Excel, forming a sampling frame of 2,272 records. To ensure random selection, each MRN was assigned a random number using the RAND function, and the list was sorted accordingly. The corresponding medical records were reviewed, and those with missing key data were excluded through listwise deletion to ensure data quality and reliability of analysis. Ultimately, 459 eligible records meeting the study’s inclusion criteria were included in the analysis. Figure 1 summarizes the sampling process.

Fig. 1

A schematic representation of the sampling procedure for the study predictors of time to recovery from cataract surgery among cataract patients at Menelik II Comprehensive Specialized Hospital, 2024

Variables

Dependent variable

The dependent variable was the time to recovery from cataract surgery, defined as the duration (in weeks) from the date of cataract surgery until recovery was achieved.

Independent variables

Independent variables included the following:

Sociodemographic variables

age, sex, and residence.

Preoperative factors

cataract type (e.g., age-related, traumatic, congenital), level of visual acuity, ocular comorbidities (e.g., age-related macular degeneration (AMD), glaucoma, diabetic retinopathy), and systemic comorbidities (e.g., diabetes mellitus, hypertension, cardiovascular diseases).

Intraoperative factors

Cataract surgery type (e.g., phacoemulsification, manual small-incision cataract surgery), type of anesthesia (e.g., local, general), site of lenses inserted (IOL), intraoperative complications (e.g., intraocular lens dislocation, posterior capsule rupture, vitreous loss) and surgeon status.

Postoperative factors

postoperative complications (e.g., infection, inflammation, cystoid macular edema, secondary glaucoma, or retinal detachment).

Operational definition

Recovered

Recovery from cataract surgery was defined as the clinician-assessed restoration of vision (≥ 6/12), resolution of discomfort, and ocular healing. Resolution of discomfort referred to the absence or significant reduction of postoperative symptoms, including pain, foreign body sensation, excessive tearing, photophobia, and itching, as reported by the patient and confirmed by the clinician during follow-up. Ocular healing was determined by the absence of inflammation or infection during postoperative evaluations. Recovery also included patient satisfaction with vision and the ability to resume daily activities [32, 33].

Start time

the date when surgery was performed.

Follow-up period

From the date of surgery until either to a point where an event or censorship occurs.

The time to recovery

was defined as the duration in weeks from the date of cataract surgery until the patient achieved full recovery.

Event

the occurrence of full recovery.

Censored

A cataract patient who was treated with cataract surgery but whose outcome was out of follow-up (> 26 weeks), who was lost to follow-up, or who was transferred to other health facilities.

Loss to follow-up

refers to patients who visited the ophthalmic outpatient department (OPD) three times, were scheduled for a subsequent visit, but did not attend that visit, and whose final outcomes were not recorded.

Transferred-out

Patients who moved to another health facility with a confirmed written documentation of a transfer out.

Visual acuity

ability to resolve detail at 6 m on a Snellen chart [34]. In this study, the participants were categorized as having low acuity (worse than 6/60), medium acuity (6/18 to 6/60), or high acuity (6/12 to 6/18).

Ocular comorbidity

refers to the presence of one or more additional eye conditions or diseases alongside cataracts in a patient, including age-related macular degeneration (AMD), glaucoma, diabetic retinopathy, retinal detachment, corneal diseases, refractive errors (e.g., myopia, hyperopia, astigmatism), and optic nerve disorders.

Systemic comorbidity

refers to the presence of other medical conditions or diseases that a cataract patient may have in addition to cataracts, including diabetes mellitus, hypertension, cardiovascular diseases, respiratory disorders, neurological conditions, autoimmune diseases, renal and liver diseases, cancer, and metabolic disorders.

Intraoperative complications

were assessed by ‘Yes’ or ‘No’ questions and were considered present if the cataract patients experienced adverse events or issues that occurred during the surgical procedure itself, such as intraocular lens dislocation, posterior capsule rupture, corneal edema, or vitreous loss.

Postoperative complications

were assessed by ‘Yes’ or ‘No’ questions and were considered present if the cataract patients had adverse events that occurred after the surgical procedure was completed, typically within the immediate recovery period or during the healing process, such as infection, inflammation, delayed wound healing, cystoid macular edema, secondary glaucoma, or retinal detachment.

Data collection and quality assurance

Data were extracted from the electronic medical records system and patient cards of the hospital using a structured checklist adapted from a previous study [27]. Before data collection, the tool was pretested on 5% of the sample size. Two health management information system (HMIS) officers collected the data using the Kobo collection tool under the supervision of an ophthalmic nurse employed at the hospital. Data collectors and supervisor received two days of training on the study purpose, ethical considerations and data extraction protocols. Relevant information, including socio-demographic characteristics, preoperative assessment, intraoperative details, postoperative complications, and recovery outcomes, was systematically extracted. Incomplete follow-up records were excluded. To further ensure data quality, a 10% random sample of the extracted data was re-extracted by the supervisor to verify consistency, and necessary corrections were made. Daily supervision by the supervisor and the principal investigator ensured the completeness, consistency and timely submission of collected data to the Kobo database. Data cleaning was performed post-entry using frequency, cross-tabulation, sorting, and listing to identify missing values and outliers. Any identified errors were corrected by cross-referencing with the original documents.

Data processing and statistical analysis

Upon completion of the data collection activities, the dataset was exported to Excel and imported into STATA version 17 for analysis. Descriptive statistics summarized baseline characteristics. Continuous variables were assessed for normality using histograms and the Shapiro-Wilk test. Normality was violated (p < 0.001); therefore, the data are presented as medians with interquartile ranges (IQR). Categorical variables are summarized as frequencies and percentages.

Recovery time was calculated as the difference between the surgery and recovery dates. Due to the nature of follow-up documentation, it was assumed that some recovery times might be interval-censored, occurring between scheduled visits. To address these potential censoring, a parametric Weibull regression model was used. This model, assuming a Weibull distribution for recovery times, estimated the likelihood of recovery within intervals rather than at specific moments, enabling a more accurate estimation of recovery patterns in the study population. Four-week interval recovery probabilities were estimated using a life table. The Kaplan‒Meier survival estimator was used to determine the median recovery time and to generate survival curves for comparing recovery times across groups. The log-rank test was used to assess significant differences in survival functions between these groups.

We assessed the proportional hazards and multicollinearity assumptions to ensure model validity. The proportional hazards assumption was confirmed using the Schoenfeld residuals global test (p = 0.421). Multicollinearity was evaluated using the variance inflation factor (VIF), with all values below the threshold of 10 (highest VIF = 9.54). No multicollinearity violations were detected. Detailed results are provided in Table 6 of electronic supplementary material.

To identify the best-fitting model, survival models were compared using log-likelihood, Akaike information criterion (AIC), and Bayesian information criterion (BIC) values. The multivariable Weibull regression model with an inverse Gaussian frailty component was selected as the preferred model (log-likelihood = -173.09, AIC = 398.19, BIC = 505.55). See Table 7 of electronic supplementary material for details. This model accounts for individual variation in recovery times and unobserved heterogeneity by incorporating the Weibull distribution for recovery time estimation and the inverse Gaussian frailty to adjust for unmeasured heterogeneity. It also controls for potential confounding factors by addressing variability across individuals that may influence recovery time.

Predictors of time to recovery were identified using bivariable and multivariable Weibull regression with inverse Gaussian frailty models. Initially, a bivariable Weibull regression model with a crude hazard ratio was used to identify candidate variables for the multivariable Weibull regression model at a P value < 0.25. All identified candidate variables were subsequently incorporated into the multivariable Weibull regression model. The findings were presented using adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) to measure the strength of the association, and statistical significance was declared at a P value < 0.05. Variables that were statistically significant in the multivariate analysis were considered independent predictors of time to recovery from cataract surgery.

Results

Sociodemographic characteristics of the study participants

The records of 494 cataract patients who underwent surgery in 2023 were reviewed. Of these, 35 patients (7.1%) had incomplete medical records and were excluded, leaving 459 (92.9%) for analysis. Among the included patients, 234 (50.98%) were male, and nearly two-thirds (293/459; 63.83%) resided in urban areas. The participants’ ages ranged from 1 to 94 years, with a median age of 60 years (IQR = 21 years). A large proportion of the patients, 207 patients (45.10%), were over 60 years old, whereas 146 (31.81%) were aged 46 to 60 years (Table 2).

Table 2 Sociodemographic characteristics of cataract patients treated with cataract surgery at menelik II comprehensive specialized hospital from January 1, 2023, to December 31, 2023(n = 459)

Preoperative characteristics of the study participants

Age-related cataracts were the most prevalent cataract, affecting 68.85% (316) of the patients. Over two-thirds of the patients 68.63% (315) had low visual acuity before surgery. Ocular comorbidities were present in 23.97% of patients, with glaucoma being the most common (66.36%). Systemic comorbidities affected 20.70% of the patients, with hypertension being the most prevalent (69.47%), followed by diabetes mellitus (33.68%). See Table 3 for details.

Table 3 Preoperative characteristics of cataract patients treated with cataract surgery at menelik II comprehensive specialized hospital from January 1, 2023, to December 31, 2023(n = 459)

Intraoperative characteristics

Small-incision cataract extraction (SICE) was the most common (73.42%) surgical procedure, and local anesthesia was predominantly (92.59%) used. The majority (91.50%) of lens insertions occurred in the posterior chamber. A greater proportion of surgeries were performed by ophthalmologists (54.47%) than by ophthalmology residents (45.53%). Intraoperative complications occurred in 8.71% of the patients, with capsular tears being the most commonly observed, accounting for 50% of those with complications. See Table 4 for details.

Table 4 Intraoperative characteristics of cataract patients treated with cataract surgery at menelik II comprehensive specialized hospital from January 1, 2023, to December 31, 2023(n = 459)

Postoperative characteristics

Among the 459 patients, 53 (11.55%) experienced postoperative complications. The most common complications were inflammation 12 (22.64% of cases) and increased intraocular pressure (IOP) 11 (20.75% of cases). Other notable complications included posterior capsular opacification (PCO) in 8 patients (15.09% of cases) and infection in 7 patients (13.21% of cases). The less frequent complications were retinal detachment (6, 11.32%), secondary glaucoma (5, 9.43%) and others (cystoid macular edema and cataract lens fragments) (5, 9.43%). Overall, the majority of patients (406; 88.45%) did not experience any postoperative complications.

Time to recovery of patients who underwent cataract surgery

Overall, 459 cataract patients were followed for a total of 7,919.28 person-weeks. At the end of the study period, 368 (80.17%) patients had recovered after cataract surgery. The overall incidence rate of recovery was 46.47 (95% CI: 41.95–51.47) per 1000 persons per week. The overall median recovery time of patients was 18.14 weeks (IQR = 12.29–24), with (95% CI = 17.14–18.86) (Fig. 2).

Fig. 2

Kaplan‒Meier estimator curve for time to recovery among cataract patients at Menelik II Comprehensive Specialized Hospital from January 1, 2023, to December 31, 2023

The life table showed a gradual decrease in survival probability (i.e., not yet recovered) after cataract surgery, from 0.9978 at 4 weeks to 0.7665 at 8–12 weeks, 0.4150 at 16–20 weeks, and 0.1209 at 24–28 weeks, indicating that most recoveries occurred between 8 and 20 weeks (Table 5).

Table 5 Life table of patient survival probabilities at 4-week intervals for the time to recovery from cataract surgery at menelik II comprehensive specialized hospital from January 1, 2023, to December 31, 2023

Time to recovery among different groups of cataract patients

Kaplan–Meier survival curves and log-rank tests were used to assess differences in recovery times across categorical variables. Shorter recovery was observed among younger patients (31–45 years: 13.43 weeks, P = 0.0007), married individuals (17.71 weeks, P < 0.0246), urban residents (16.85 weeks, P = 0.0004), patients with secondary cataracts (12.43 weeks, P < 0.0001), those undergoing phacoemulsification (13.43 weeks), patients with high preoperative visual acuity (12.43 weeks, P < 0.0001), and surgeries performed by ophthalmologists (14.29 weeks, P < 0.0001). Patients without comorbidities or complications also had significantly shorter recovery times (P < 0.001). No significant differences were found by sex, anesthesia type, lens insertion site, or the eye operated on. Kaplan–Meier curves (Fig. 3, electronic supplementary material) and log-rank test results (Table 8, electronic supplementary material) provide further details.

Predictors of time to recovery from cataract surgery

In the initial analysis, a bivariable Weibull regression with inverse Gaussian frailty was used to determine the effect of each covariate on the time-to-recovery before proceeding to the multivariable analysis; at a value of p < 0.25 relaxed level of significance. The analysis revealed that age, marital status, residence, level of visual vacuity, type of cataract, presence of ocular comorbidities, presence of systemic comorbidities, type of cataract surgery, site of lens insertion, presence of intraoperative complications, status of the surgeon and presence of postoperative complications had significant effects on recovery time at this significance level.

After the first analysis, a multivariable Weibull regression model with an inverse Gaussian frailty component was employed at a significance level of p < 0.05 to adjust for confounding variables while accounting for unobserved heterogeneity among individuals. Patients over 60 years of age had a 75% slower recovery rate compared to those under 16 years (AHR = 0.25, 95% CI: 0.07–0.96). Whereas, patients living in urban areas had a 77% higher recovery rate than those in rural areas (AHR = 1.77, 95% CI: 1.15–2.70). Patients with a medium level of preoperative visual acuity experienced a 98% faster recovery time compared to those with low visual acuity (AHR = 1.98, 95% CI: 1.23–3.18), whereas patients with high visual acuity experienced a more than fivefold faster recovery time (AHR = 5.83, 95% CI: 1.72–19.77). Patients with ocular comorbidities had a 70% slower recovery rate (AHR = 0.30, 95% CI: 0.15–0.61), whereas those with systemic comorbidities had a 59% slower recovery rate (AHR = 0.41, 95% CI: 0.22–0.75).

Patients who underwent phacoemulsification surgery were associated with a 98% higher recovery rate compared to those who underwent small-incision cataract extraction (SICE) (AHR = 1.98, 95% CI: 1.06–3.67). In contrast, patients who underwent intracapsular cataract extraction (ICCE) were significantly associated with an 86% slower recovery time (AHR = 0.14, 95% CI: 0.03–0.89). Patients who underwent surgery by ophthalmologists experienced more than three times faster recovery rates (AHR = 3.44, 95% CI: 1.80–6.55) than those who underwent surgery by residents. Patients with intraoperative complications had a 71% slower recovery compared to those without complications (AHR = 0.29, 95% CI: 0.12–0.71), and patients with postoperative complications experienced an 83% slower recovery compared to those without postoperative complications (AHR = 0.17, 95% CI: 0.06–0.47). A significantly high degree of frailty (θ = 2.23) was observed, indicating substantial unobserved heterogeneity in recovery rates among patients. This suggests that unmeasured factors (e.g., patient characteristics not included in the model) contributed significantly to differences in recovery rates between individuals. See Table 9 of electronic supplementary material for details.

Discussion

In this study we assessed the time to recovery following a cataract surgery and its predictors among patients. The overall median recovery time was 18.14 weeks, with 80.17% of patients recovering, and an incidence rate of 46.47 per 1000 person-weeks. Age, residence, preoperative visual acuity, the presence of comorbidities, the type of cataract surgery, the presence of complications, and the surgeon status were independent predictors of recovery time.

The median recovery time observed in this study was longer than the recovery periods reported in developed countries, where guidelines suggest that recovery typically occurs between 4 and 12 weeks [21, 22]. Similarly, the American Academy of Ophthalmology reported that full recovery is usually achieved within 4–8 weeks [35]. This difference can be attributed to variations in patient demographics, surgical techniques, and complication rates between the populations studied [36]. Phacoemulsification, widely used in developed countries, involves smaller incisions, less tissue trauma, and faster visual rehabilitation [37]. However, its adoption in Ethiopia remains limited due to high equipment costs, the need for specialized training, and infrastructural challenges [24]. These barriers contribute to the continued reliance on alternative techniques such as small-incision cataract surgery (SICS) and extracapsular cataract extraction (ECCE), which may lead to longer recovery periods.

Compared to a previous study conducted in Northern Ethiopia, which reported a median recovery time of 23 weeks [27], this study observed a shorter recovery period. The difference may be explained by the setting of this study, which was conducted at a tertiary national referral facility in Ethiopia, where severe cases are more commonly managed, potentially leading to prolonged recovery times. However, the use of advanced surgical techniques, such as phacoemulsification, and the expertise of the surgeons may have mitigated the impact of case complexity, resulting in relatively faster recovery compared to other local studies.

Patients over 60 years old had a 75% slower recovery compared to those younger than 60. This finding aligns with those of previous studies that have consistently shown that older age is associated with slower recovery after cataract surgery [38, 39]. Age-related physiological changes, such as reduced wound healing capacity and the presence of multiple comorbidities, are more common in older adults and may contribute to slower recovery. This highlights the importance of tailored clinical approaches for older patients to address their unique needs during the postoperative period. Compared with rural residents, urban residents had a 77% faster recovery. This finding is consistent with studies conducted in other areas, which suggest that urban residents generally have better access to healthcare services, including follow-up care, which likely contributed to their faster recovery [27, 40]. Furthermore, urban residents may benefit from less physically demanding jobs, making it easier to adhere to postoperative restrictions and their proximity to healthcare facilities allows for regular monitoring and timely interventions.

This study indicates that preoperative visual acuity significantly influences recovery time. Patients with medium preoperative visual acuity recovered 98% faster than those with low acuity do, whereas those with high acuity recover more than five times faster. The supporting literature reinforces these findings, suggesting that initial visual acuity is a crucial determinant of recovery time. Studies conducted in Ethiopia and Malaysia link better preoperative acuity to improved postoperative outcomes and faster recovery [27, 41]. Similarly, studies from China have shown that patients with high preoperative visual acuity are more likely to achieve favorable postsurgery vision [42, 43]. These insights underscore the critical need for preoperative evaluations to optimize surgical outcomes and recovery trajectories.

Cataract patients with systemic comorbidities had a 59% slower recovery rate than did those without comorbidities, a finding consistent with studies from Malaysia, which reported similar impacts of comorbidities on postsugary recovery [41]. Similarly, ocular comorbidities were associated with a 70% reduction in the recovery rate, which aligns with studies from Ethiopia [44], and The Review of Ophthalmology, which highlighted the challenges posed by glaucoma and other eye conditions to recovery [45]. The slower recovery of these patients may be attributed to the additional burden of managing multiple health issues, complicating the recovery process. These findings emphasize the need for clinicians to address both systemic and ocular comorbidities when planning and managing postoperative care to optimize recovery outcomes.

Patients who underwent phacoemulsification recovered 98% faster than those who underwent small-incision cataract surgery (SICS), consistent with studies from Japan and India, which reported fewer complications and less trauma with phacoemulsification [46, 47]. However, a study on diabetic retinopathy patients indicated that visual improvement might not depend on the surgical technique used [48]. Patients who underwent intracapsular cataract extraction (ICCE) experienced an 86% slower recovery rate than did those who underwent small-incision cataract surgery (SICS), aligning with studies linking intracapsular cataract extraction (ICCE) to higher complication rates and delayed recovery [49]. The faster recovery with phacoemulsification may be attributed to its less invasive nature, minimizing trauma and promoting faster healing. In contrast, slower recovery following intracapsular cataract extraction (ICCE) may be due to larger incisions and sutures, leading to complications like corneal edema and delayed wound healing.

Patients who undergo cataract surgery by ophthalmologists recover more than three times faster than those treated by residents do. This highlights the importance of the surgeon’s expertise in ensuring optimal recovery. Studies from the United States, Nigeria, and Israel have revealed that greater surgical experience leads to fewer complications, reduced postoperative corneal edema, and faster recovery [50,51,52]. This emphasizes the need for prioritizing experienced surgeons in cataract surgery to enhance patient recovery and surgical outcomes.

This study also revealed that intraoperative and postoperative complications significantly affect recovery times. Intraoperative complications led to a 71% slower recovery, and postoperative complications caused an 83% slower recovery than in patients without complications. These findings align with studies from Spain and India, which reported delayed recovery due to intraoperative events, such as posterior capsule rupture [53, 54]. Similarly, postoperative complications have been associated with poorer outcomes and prolonged recovery in regions like Finland and the United Kingdom [55, 56]. The slower recovery may result from the need for additional interventions to manage complications, prolonging the healing process. The delayed recovery may be due to the need for additional interventions to address complications, which prolong the healing process. These findings highlight the importance of adopting meticulous surgical techniques and vigilant postoperative care to minimize complications and improve recovery outcomes.

This study’s findings provide important insights into the factors influencing recovery time after cataract surgery in Ethiopia, emphasizing the need for tailored interventions to optimize recovery based on key predictors like age, residence, and preoperative visual acuity, type of surgery, comorbidities, and surgeon expertise. The study also highlights the need for future research exploring the impact of advanced surgical techniques and postoperative care adherence on recovery outcomes. Understanding these factors in greater depth could inform clinical guidelines and improve patient care, especially in resource-limited settings.

Strengths and limitations of the study

The strength of this study lies in the use of a Weibull survival model with an inverse Gaussian frailty component, which allowed for the assessment of both observed and unobserved factors affecting recovery time. The use of Kobo Collect enhanced data accuracy and quality through built-in validation rules.

However, the retrospective nature of this study limited the inclusion of certain key variables, such as socioeconomic status, level of patient education, occupational demands, lifestyle habits, dietary patterns, surgery duration, post-operative refractive surprise and adherence to postoperative car, all of which may have influenced recovery but were not available for analysis. This introduces the potential for residual confounding. Furthermore, we acknowledge that postoperative recovery was defined based on clinician assessment, which, while consistent with clinical practice in our setting, introduces subjectivity and may not be universally applicable. Objective parameters such as refractive stability, corneal edema resolution, cessation of postoperative medications, and uveitis resolution, which could provide a more standardized recovery endpoint, were not available in the retrospective data and are now noted as study limitations.

The exclusion of incomplete medical records may have introduced selection bias, as patients with incomplete follow-up data might have had different recovery trajectories compared to those included in the analysis. The presence of interval-censored data, where recovery times were unknown between follow-up visits, also introduces some uncertainty into the analysis. Lastly, the use of a single-facility data source limits representativeness and generalizability. These limitations should be considered in future research and clinical practice.

Conclusions and recommendations

The median recovery time from cataract surgery in this study was longer than in developed countries. Recovery time was significantly influenced by patient age, residence, preoperative visual acuity, comorbidities, surgical techniques, surgeon expertise, and complications. Older patients, those with comorbidities or complications, and those undergoing less advanced surgeries like intra capsular cataract extraction had slower recovery rates.

To enhance recovery times, efforts should focus on improving access to phacoemulsification, strengthening preoperative assessments, improving healthcare access for rural populations, and minimizing intraoperative and postoperative complications, and enhancing surgeon expertise to ensure high-quality surgical performance. Future multi-center, prospective studies are needed to validate these findings and further investigate additional factors affecting postoperative recovery.