Time to elective surgery and its predictors after first cancellation at Debremarkos Comprehensive Specialized Hospital, Northwest Ethiopia

Canceling elective surgical procedures is quite common throughout Ethiopia. Despite this, there is limited evidence about the time to elective surgery after cancellation in the country. Thus, the current study aimed to determine the time to elective surgery and its predictors after the first cancellation. An institution-based retrospective follow-up study was conducted on 386 study participants at Debre Markos Comprehensive Specialized Hospital, Northwest Ethiopia, between September 1, 2017, and August 31, 2022. Utilizing a checklist, data were retrieved. To choose study participants, systematic random sampling was employed. Epi-Data version 3.1 and STATA version 14.1 were utilized. Kaplan-Meier curves and log-rank tests were employed. The Cox proportional hazard model was fitted. The mean age of the participants was 41.01 + 18.61 years. Females made up 51% of the patients. The majority were illiterate (72.3%) and resided in rural areas (70.5%). Surgery following the first cancellation had a cumulative incidence of 83.6% (95% CI: 79.6, 87.05) and an incidence rate of 32.3 per 1,000 person-days (95% CI: 29.3, 35.5). The median survival time to surgery was 25 (IQR: 17–40) days. Urban residence (AHR = 1.62; 95% CI: 1.26–1.96), being a member of health insurance schemes (AHR = 1.55; 95% CI: 1.24–1.96), stable other medical conditions (AHR = 1.43; 95% CI: 1.13–1.79), and timely completion of diagnostic tests (AHR = 1.62; 95% CI: 1.29–2.04) were significant predictors of time to surgery after first cancellation. Our study revealed that the time to surgery after the first cancellation was in the globally acceptable range and met the national target. Clinicians should focus on timely completion of diagnostic or laboratory tests, facilitating health insurance coverage, and comprehensive assessment and treatment of any coexisting medical conditions. It is urged to stratify each department’s time for surgery, taking into consideration of important variables.

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INTRODUCTION
Elective surgery is defined as elective non-emergency surgery that is medically necessary but can be delayed for at least 24 hours (1).Although these surgeries are optional, they are often important and potentially life-changing, and some are same-day surgeries that do not require a hospital (2, 3) Worldwide, 3.5% of surgical procedure is performed by patients who need elective surgery, and one-third of the procedure is undertaken after the patient experience at least one cancelation (1, 4).
Because this is a global problem, 29% of elective surgeries were completed after patients experienced at least one cancellation, with an overall average waiting time of 21 days, the WHO report found (5).
Elective surgery after the 1st cancellation refers to surgery performed after the patient cancels once on the scheduled date of surgery (6).Long waiting times for surgery have long been a common problem worldwide (6, 7).After the first cancellation, the burden of waiting before surgery was only 15% in Europe and 7% in Africa (8).In developing countries, including Ethiopia, wait times for surgery after the first cancellation are common, which also increases the risk of death in hospitals (9).More than 72% of reasons to wait long for surgery after cancellation can be avoided (10).If the waiting time is long, the operating room and time cannot be used properly, causing inconvenience to patients and their families and causing psychological trauma to both patients and their families (11,12).
Previously, time-to-surgery among select cases on the African continent, particularly Ethiopia, focused primarily on time from eligibility to surgery (4).Time to surgery is an excellent measure of the quality of hospital surgical management (13).Unlike Western countries, few studies have evaluated time to surgery and its predictors among elective cases after first cancellation in the country, Ethiopia; even we could not find a similar study in our current study settings.
Our observations showed that many patients suffer from severe pain, additional costs, emotional trauma, and feelings of hopelessness, and eventually, the patient may die if the operation is canceled again on the scheduled date.
Better assessment and understanding of time to surgery and predictors after the first due date cancellation will reduce subsequent complications in cases, re-surgery, and over-hospitalization, and improve the quality of care for patients who have undergone elective surgery.
So, this study was designed to evaluate time to surgery and its predictors among elective surgery cases after the first cancellation in Debre Markos Comprehensive Specialized Hospital, Northwestern Ethiopia.
This study will help clinicians and other service providers design interventions to reduce surgical time in hospitals by removing predictable factors that could be avoided.

Study Setting, Design, and Populations
A Five-year hospital-based retrospective follow-up study was conducted from September

Sample Size Determination and Sampling Procedure
The sample size was calculated by using the sample size determination formula for survival analysis.It is calculated by taking the two-sided significant level of () of 5%, power 80%

Sampling Technique and Sampling Procedure
All patients scheduled to undergo elective surgical procedures from September 1, 2017, to August 31, 2022, at Debre Markos Comprehensive Specialized Hospital were identified.The patient's medical card number was extracted from the scheduled cancellation registration book.There were a total of 1158 admitted elective surgical patients who experience at least one cancelation within four years.The 386 samples were proportionally allocated for each year and with systematic random sampling, the study participants of each year were selected as follows.First, numbering the unit of each year on the frame from 1 to N (N= total admission of each year), then we determine the sample interval (K) by dividing the number of units in the population by the desired sample size of each year (n=sample size of each year) (Figure 2).

Operational Definition
An adequate number of surgeons: is defined as assigned senior specialists in each operation suite and even if the team was not busy with emergency surgery (16).
Adequate number of nurses: is defined as assigned scrub and circulating nurse in each operation suite and even if the team was not busy with emergency surgery (16) Adequate number of anesthetists: is defined as the presence of an assigned anesthetist in each operation suite and even if the team was not busy with emergency surgery (16) Censored: patients who were referred to other hospitals after the first cancellation, died after the first cancellation and before surgery, against surgery after the first cancellation, and were absent for call to surgery.
Elective surgery: is non-emergency surgery that is medically necessary, but which can be delayed for at least 24 hours (17).
Event (failure): Elective surgery is done after the first cancellation.

First day of surgical cancellation: not being done with the first appointed day
Intended day: the specific and planned day when the operation is to be performed.
Major surgery: Surgery involving risk to the life of the patient specifically an operation on an organ within the cranium, chest abdomen, and pelvic cavity (18) Minor surgery: a set of procedures in which short surgical techniques are applied to superficial tissue, usually with local anesthesia (18).

Survival time:
The time in days from the date of first cancellation to the date of the event or time of censoring.
Time to surgery: is defined as the time from the first day of cancellation of the elective case to the surgery done within days.

Outcome variable
Time to surgery after the first cancellation daily evaluation of the data for completeness and encountered difficulties at the time of data collection was attended to accordingly.Finally, all the collected data were checked for completeness and consistency during the data management, storage, and analysis.

Data Processing and Analysis
The collected data were checked for clarity, consistency, and completeness up to the end of the The potential predictors to the full model were selected by bi-variable Cox proportional hazard regression with the cut of point P<0.25.The multi-collinearity for variables was checked using the variance inflation factor (mean VIF) and it was 1.12 indicating little or no collinearity between independent variables.The association between predictors and hazard of surgery was summarized using an Adjusted Hazard Ratio (AHR), and statistical significance was tested at P<0.05.The survival status and in-hospital surgery rate of the elective case after the first cancellation were estimated.

ETHICAL CONSIDERATION
The ethical principles were consistent with the Declaration of Helsinki.Ethical clearance was obtained from the Institutional Research Ethics Review Committee of Debremarkos University with reference number: HSC/R/C/Ser/PG/Co/50/11/14.The ethical letter was given to Debre Markos Comprehensive Specialized Hospital to get permission for the data collection process.A permission letter was obtained from Debre Markos Comprehensive Specialized Hospital.The necessary explanation regarding the purpose of the study was informed to data collectors and the concerned official bodies in the hospital.Confidentiality of the information was assured by not recording patients' names from the chart and privacy of the information was maintained.Informed written consent was not taken as secondary data were extracted from patient charts.

Sociodemographic and Other Characteristics
The analysis was performed for a total of 386 patients who met the inclusion criteria from September 1, 2017, to August 31, 2022.The mean age of the participants was 41.01+18.61and with a range from 1 year to 85 years.The majority of the elective surgery cases, the one hundred and sixty-three (42.23%) cases after cancellation came from the surgical department, followed by one hundred and twenty-nine (33.42%), seventy-two (18.65) %) and twenty-two (5.7%) patients from the respective departments of Gynecology, Orthopedics and Obstetrics (Table 1 and Table 4).

Survival Status and Survival Function of Elective Surgical Patients
The overall Kaplan-Meier estimate showed that the probability of surgery of elective case surgery was a long duration on the first day after the first cancellation and progressively short time as the follow-up time increased as shown in the figure below (Figure 3).

Survival Function and Comparison of Different Categories
Kaplan-Meier estimator survival curve gives the estimate of survivor function among different groups of variables to make comparisons.Separate graphs of the estimates of the Kaplan-Meier survivor functions were constructed for categorical variables as described below.In general, the pattern that one survivorship function lying lower than another means the group defined by the lower curve has a short time to elective surgery than the group defined by the upper curve or had a more favorable survival experience than the group defined by the upper.But the statistical question is whether the observed difference seen on the plot is significant or not.This can be shown by the log-rank test.
The log-rank test was conducted to check for the existence of significant differences in survival among various levels of the categorical predictors considered in the study.The Kaplan -Meier analysis indicated significant evidence of differences in survival times in different categories among different predictor variables as shown below.
The Kaplan-Meier survival curves below showed those elective surgical patients after the first cancellation of urban residence had a short time of surgery than elective surgical cases after the first cancellation from rural residence (Figure 4).
The Kaplan-Meier survival curves below showed that elective surgical patients after the first cancellation with laboratory investigation had a short time to surgery than elective surgical patients from non-investigated (Figure 5).
The Kaplan-Meier survival curves below showed those elective surgical patients first cancellation being member of health insurance had a short time for surgery than elective surgical patients after the first cancellation of not being a member of health insurance (Figure 6) The Kaplan-Meier survival curves below showed those elective surgical patients after first cancellation with stable medical conditions had a short time to surgery than elective surgical patients with no stable medical conditions (Figure 7).

Time to Surgery in Elective Surgical Patients after First Cancellation
Three hundred eighty-six study participants were followed in the study period, and the total person day of observation was 10013.The in-patient rate of surgery was 83.67% or 3 per 100 major surgery-day observations and 16.33% of elective cases were not done and the outcome of those in this study,50% was unknown, 26.65% were referred to other hospitals, 18.75% of cases were not operated with known unstable medical conditions and 7.81% died before the operation was performed.The median time to surgery was 25 days with an IQR of 17-40 days.The mean survival time for elective surgical patients after the first cancellation was 30.62 (95% CI 28.56-32.67)days.

Predictors of Time to Surgery for Elective Cases after First Cancellation
The Cox proportional hazard model showed that the sex, patient residence, educational status, occupational status, marital status, health insurance membership status, patient medical conditions, laboratory investigations, presence of cross-matched blood, availability of surgical instrument set, presence of oxygen and presence of assigned surgeon were candidate variables in the bivariable analysis with p-value < 0. have a short time to surgery among elective surgical patients after the first cancellation, which was statistically significant.Among patients with elective surgery for whom there is a laboratory study, the probability of surgery at any given time point is almost 1.62 times (AHR = 1.62; 95% CI 1.29, 2.04) higher shortly before surgery than in patients without the study, by controlling the effect of other variables.The operation time at any point in elective surgery patients who were members of a health insurance scheme was 1.55 times shorter than that of elective surgery patients without health insurance (AHR=1.55,95% CI 1.24, 1.96).Another statistically significant variable in this study was stable health status, which was 1.43 times (AHR=1.43;95% CI 1.13, 1.79) higher than patients without stable health (Table 6).

Assessing the Fitness of the Final Model
Model fitness was assessed using the Cox-snail residual test.The cox-snail residual test showed that the hazard function follows closely the 45-degree line except for a large value of time and it is common for models with censored data to have some wiggling at a large value of time which shouldn't be much concern.This was understood that it had approximately an exponential distribution with a hazard rate of one and that the model fits the data well (Figure 8).

DISCUSSION
This study provided the first evidence of predictive factors for time to surgery after first cancellation among elective surgery cases admitted to Debremarkos Comprehensive Specialized Hospital.There were 323 (83.6%) [95% CI: 79.6, 87.05] surgeries, or 3 out of 100 major surgeries-day observation after the first cancellation.Residence in an urban area, the status of ordered laboratory tests, health insurance membership, and stable medical condition were considered important predictors of time to surgery after the first cancellation.
In terms of rates of surgery after first cancellation, this is comparable to studies done in Addis Ababa, specifically studies conducted at Zewditu Memorial Hospital in Ethiopia (86% [16], compared with studies done in the UK at 80% [22]), The study conducted in Iran hospital of Social Security Organization was 87% [21].The study conducted in Uganda, especially at Mulago Hospital was 80% (1).The mean survival time was 30.62 days [95% CI: 28.56; 32.67), longer than the time outlined in FMOH (Federal Department of Health) guidelines for saving lives through safe surgical care and anesthesia (SALT) with a waiting time of 14.5 days (13).
On the other hand, the surgery rate of elective cases after the first cancellation indicated in the present study is lower than the studies reported from Canada 92% (20), the studies in Burkina Faso (89%) (25), the studies Mahatma Gandhi medical college, Ethiopia (95%) (26).A higher level of elective surgery in developed countries may be related to the quality of operating theaters, sufficient staff training, level of public awareness, up-to-date equipment, and high availability of services.However, the rate of surgery after the first cancellation obtained from this study is higher than the study conducted in Ghana, specifically Comfy Anokye teaching hospital (50%) (27), Zambia (77%) (28), and study in Tikur Anbesa Specialized Hospital, Ethiopia (70.8) (29).The discrepancy may be due to differences in study duration, study population, and duration of some studies, another reason may be low patient flow in the study setting.In this study, the urban residence was found to be an important predictor of surgery done after experiencing first cancellation among elective surgical patients compared to rural areas.This finding is in agreement with other previous studies conducted in Portuguese (15), study in Tanzania (30), study in a specialized hospital in Ethiopia (17).
For those elective surgical cases after the first cancellation, ordered laboratory investigation being done was observed as an independent predictor of surgery after the first cancellation, when compared to patients not done as ordered.This is in agreement with previous studies in Wales (31), and a study in Nigeria (32).Despite sending general laboratory tests for each scheduled surgical case before surgery, the study confirmed that it was not completed on time as directed due to long waiting times.
In addition, health insurance coverage was another predictor of scheduled intraoperative surgery after initial cancellation compared to those without health insurance.This finding is supported by different studies conducted in Australia (33), and the study in Nigeria (32).These people were more likely to have surgery after their first cancellation because they paid per family per year at a lower or lower rate, making them more likely to go to public hospitals out of pocket for each visit.[26,28].
Moreover, the stable state of health before elective surgery is another predictor of surgery, and among the elective cases after the first cancellation, patients with stable health before surgery were an important predictor.This study is in line with the study conducted by Hawasa Comprehensive Specialized Hospital, Ethiopia (34), and with a study conducted in Portuguese (15).A stable medical condition is a major concern before elective surgery, as it causes difficulties in anesthesia induction and leads to re-cancellation delaying the actual surgical time.
The retrospective nature of the present study and the incomplete history of patients undergoing elective surgery were limitations of the study.The problem of missing data can lead to random error and patients with missing data are systematically different from patients with complete data.
Additionally, some patients may be denied phone calls and some surgeries may be performed at other nearby hospitals.Loss of follow-up may have affected the rate of hospital surgery.

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Variables Socio-demographic factors: age, sex, residency, occupation, religion, marital status, educational status, and employment status Hospital administration-related factors: availability of recovery bed, consistency of power, availability of operation room supply including all surgical instrument set, presence of anesthesia drug supply, enough OR table, oxygen availability, and presence of cross-matched blood Patient-related factors: patient acceptance, family or caregiver acceptance, other stable medical conditions, the patient being NPO, whether ordered laboratory investigation done on time, being a member of health insurance schemes, and the responsible department (obstetrics, gynecology general surgery, and orthopedics) Health professional-related factors: an adequate number of surgeons in all specialties, an adequate number of anesthetists, and an operating room nurse Data Collection Procedures, Tools, and Data Quality Control The patient's medical record numbers were retrieved from surgery cancellation logs.Data were collected using a structured checklist prepared in English.The data extraction sheet was adapted and modified from different related studies (8, 19-24) (Figure 1) and also from different registration books.Surgical intervention was confirmed by reviewing the operation note sheet from the patient chart in the hospital.Data quality was assured by designing a proper data extraction checklist.Pretest was conducted on 5 %( 19) of the sample size at DMCSH on charts of elective surgical patients who experienced at least one cancellation to check usually recorded variables on the patient s medical record and their consistency for the actual study.One day of training was given concerning the data extraction and the data collection process for data collectors.During the data collection time, close supervision and monitoring were carried out by the principal investigator to ensure the quality of the data; data collection period.After extraction, data were coded and entered into the Epidata version 3.1 statistical software and exported to STATA version 14.1(College Station, Texas 77845 USA) for cleaning recoding categorizing further analysis.The percentage, frequency, mean and median based on the distribution of each data about all covariates were summarized by descriptive statistics.The survival experience of the patients was assessed by using the Kaplan-Meier survival function.The log-rank test was used to test the presence of significant differences among survival curves.The independent effects of predictor variables for surgery were assessed by Cox proportional hazard model.Schoenfeld residual tests, the interaction of each covariate with time, and graph methods were used to check the Cox proportional hazard (PH) assumption.The Cox PH model was done to estimate the independent effect of predictors on the occurrence of surgery after the first cancellation.Likely hood ratio (LR) was used to identify model fitness among the candidate's model and the model with high value considered well fit indicating less information lost on the data was selected and further goodness of fit of the final model was assessed by using Cox-snell residual technique.The model was built by a stepwise backward elimination procedure.

Figure 3 :
Figure 3: Overall Kaplan-Meier Survival Estimate of Elective Surgery Cases after First Cancellation in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Figure 4 :
Figure 4: The Kaplan-Meier Survival Curves Comparing Survival Time of Elective Surgery Cases of First Cancellation with Categories of Residence in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Figure 1 :
Figure 1: The Kaplan-Meier survival Curves Comparing Survival Time of Elective Surgery Cases after First Cancellation with Status of Laboratory Investigations in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Figure 1 :
Figure 1: The Kaplan-Meier Survival Curves with Log-rank Test Comparing Survival Time of Elective Surgery Cases after First Cancellation with Categories of Membership of Health Insurance in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Figure 7 :
Figure 7: The Kaplan-Meier Survival Curves with Log-rank Test comparing survival Time of Elective Surgery Cases after First Cancellation with Categories of Medical Condition in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Figure 1 :
Figure 1: Assessment of Goodness of Model Fitness by Using Cox-Snail Residual Test for Time to Surgery and Its Predictors among Elective Cases Admitted at DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022

Table 1 :
Sociodemographic Characteristics of Elective Cases after First Cancellation in DMCSH

Table 2 :
Age Category of Elective Cases after First Cancellation in DMCSH, Northwestern

Table 3 :
Hospital Administration Related Factor for Surgery after First Cancellation in DMCSH,

Table 4 :
Patient-Related Factor for Surgery after First Cancellation in DMCSH, Northwestern

Table 5 :
Health Professional-related Factor for Surgery after First Cancellation in DMCSH 25.Then multivariate Cox regression model was conducted with selected eleven predictor variables that meet the assumption.The model was fitted with backward elimination variable selection technique and four variables become significant in the final multivariate Cox proportional hazard model.The significant variable (p-value of <0.05) in the final model were residence in the urban areas, laboratory investigation, being a member of health insurance, and stable medical conditions.
The multivariable Cox proportional hazard regression model shows that, at any given point in time, urban areas were 1.62 (AHR=1.62;95% CI 1.26, 1.96) times more likely than rural areas to

Table 6 :
Bi-variable and Multivariable Cox Regression Analysis to Identify the Predictors ofSurgery among Elective Cases after the First Cancellation at DMCSH, Northwestern Ethiopia,

Figure 1 :
Conceptual Framework of Different Variables Adapted from Different Literature among Elective Cases after First Cancellation in Debre markos Comprehensive Specialized Hospital, Northwestern Ethiopia, from September 1/2017 to August 31/2022 Click here to access/download;Figure;Figure 1.docx

Proportional Allocation of Samples for Each Year Figure 1:
Schematic Presentation of Sampling Procedure to Elective Surgery Cases after First Cancellation in DMCSH, Northwestern Ethiopia from September 1/2017 to August 31/2022 Click here to access/download;Figure;