Incidence, time to recovery and predictors among neonates admitted with respiratory distress to the neonatal intensive care unit at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia, 2021

Background One of the major factors contributing to newborn morbidity and mortality across the globe is respiratory distress. In resource-constrained developing nations like Ethiopia, it is a significant issue. Depending on the quality of the care provided, the incidence and time to recovery may differ amongst medical facilities. However, Ethiopia still lacks appropriate data on the incidence and time to recovery from respiratory distress. Objective The aim of the study was to assess the incidence, time to recovery, and predictors among neonates admitted with respiratory distress in the neonatal intensive care unit at the University of Gondar Comprehensive Specialized Hospital. Methods An institution-based retrospective follow-up study design was conducted among 452 neonates with respiratory distress. Data were collected using a data extraction checklist from the medical registry. The extracted data were entered into EPI INFO version 7.2.1.0 and then exported to STATA version 14 for analysis. The median time to recovery, the Kaplan Meier curve, and the log-rank test was computed. Both bi-variable and multivariable Cox regression models were applied to analyze the data. p-value ≤ 0.05 was considered statistically significant. Results Of all respiratory distressed neonate,311 were recovered. The overall incidence rate of neonates admitted with from respiratory distress was 11.5 per 100-neonate day (95% CI: 10.30-12. 87) with 2,703-person day observation and the median time to recovery from respiratory distress was 7 days with (IQR = 3-13 days). Predictors of time to recovery from respiratory distress were very low birth weight (AHR = 0.17, 95% CI: 0.08-0.41), low birth weight (AHR = 0.50, 95% CI: 0.31-0.81), very preterm (AHR = 0.42,95% CI:0.20-0.89), sepsis (AHR = 0.50 95% CI: 0.38-0.65), hypothermia (AHR = 0.61, 95% CI: 0.39-0.81), and Apgar scores less than seven at first (AHR = 0.35, 95% CI: 0.15-0.79) and fifth minute (AHR = 0.45, 95% CI: 0.20-0.97). Conclusion The incidence and time to recovery in this study were discreetly acceptable as compared to previous study. The aforementioned predictors could be used to identify neonates with respiratory distress who are at risk of developing a long-term illness and guide prompt referral to hospitals. This will also provide clinicians with prognostic information, as longer recovery times have economic and social implications in resource limited countries like Ethiopia. The study was conducted in UOGCSH Northern Ethiopia, which is located approximately 728 km away from the capital city, Addis Ababa, 175 km from the Regional Capital, Bahir Dar. The hospital serves a population of over 7 million people in northwest Ethiopia. The NICU is a unit within the department of pediatrics and child health that provides an intensive care unit for neonates and has a capacity of approximately 40 beds at any given time. The space also accommodates invasive and non-invasive ventilations. On average, 845 births occur in the hospital each month from gestational ages of twenty-eight weeks and above, which are considered viable. The study was conducted from 01/01/2021 to 30/06/2021. An institution-based retrospective study was conducted among neonates admitted with respiratory distress in the NICU. All neonates admitted with respiratory distress to the NICU at UOGCSH were the source populations, and all neonates admitted with respiratory distress during the study period were the study populations. All neonates admitted with the diagnosis of RD in UOGCSH during the study period were included in the study. Neonates who recorded the date of admission/date of discharge and had missing charts were excluded from the study Time to recovery from RD in (days). Sociodemographic factors; residence, sex, birth weight of the neonate and gestational age. Obstetric and related factors: parity, mode of delivery, place of delivery, multiple pregnancies, PROM, preeclampsia, and placental abruption. Medical disorders in mother: gestational hypertension, maternal diabetes mellitus and human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Neonatal outcome condition: birth asphyxia, Apgar score, sepsis, jaundice, hypothermia, hypoglycemia, meconium aspiration and congenital anomalies. If a neonate was recovered from RD after completing treatment based on physician diagnosis. A period of time between the neonate’s admission by RD and his or her discharge while the neonate is recovered. It was calculated by subtracting the date of admission from the date of discharge (time in days until recovery/discharge). It refers to a neonates referred, died, transferred or defaulted from treatment. The presence of two or more of the following signs: an abnormal respiratory rate (tachypnea >60 breaths/min, bradypnea <30 breaths/minute, respiratory pauses, or apnea) or signs of labored breathing (expiratory grunting, nasal flaring, intercostal recessions, xyphoid recessions), with or without cyanosis [12]. The sample size was determined by a power approach using the sample size determination formula for survival analysis [20]: Total sample size needed (n) = (A/B)/e where n is the required sample size, Zα/2 is the critical value of the standard normally distributed variable at the 5% significance level (1.96), Zβ is the critical value of the standard normally distributed variable at the 20% significance level or type two error (0.8416), RH is the log (hazard ratio), p1 is the proportion of patients in the first category, and p2 is the proportion of patients in the second category [20] The proportion of recovered RD patients in this case was e = 0.429, based on a previous study conducted in a black lion specialized hospital [21]. Using the above formula, the sample size was calculated taking into account the following factors: PROM, sex, sepsis, maternal diabetes. Finally, the maximum appropriate sample size is obtained for PROM, which is 452.After determining the sampling fraction (k = 3), the neonates’ cards were accessed using the systematic random sampling technique, and the first card was drawn using the lottery method. The information was gathered using a checklist adapted from RD neonatal charts and similar studies (22, 23). The data collection checklist was written in English. The checklist includes information on sociodemographic characteristics of neonates with RD, as well as the mother’s maternal medical condition, neonatal medical condition, and obstetric- and gynecological-related predictors. Before collecting data, the records were reviewed, and RD neonatal cards were identified by their medical registration number. After one day of training, two BSc nurse data collectors were supervised by one BSc nurse supervisor. The data was then extracted using a structured and pretested data extraction checklist. Designing appropriate data abstraction tools ensured data quality. One day of training was also provided for both data collectors and supervisors on the data abstraction tool and data collection process. The supervisors and principal investigator closely monitored the day-to-day data collection process to ensure the checklist was complete and consistent. The data was evaluated daily for completeness, and any difficulties encountered during data collection were addressed accordingly. Finally, the supervisor and investigator double-checked all collected data for completeness and consistency during data management, storage, and analysis. Data were collected using a semi-structured checklist, and each questionnaire was checked for completeness of data, assigned a code, entered into EPI INFO 7.2, and exported to Stata14 statistical software for analysis. Prior to analysis, the data were cleaned, and missing values were handled by revising the original coded questionnaire. The median time to recovery, Kaplan Meier curve, and log-rank test were computed. The proportional hazard assumption was validated graphically as well as through Schoenfeld residual global tests. The bivariable and multivariable Cox regression models were used to describe the association between the dependent and independent variables, as well as independent predictors of time to recovery. To control for potential confounding covariates at the same time, covariates with a P-value of 0.05 in bivariate analysis were entered into a multivariable regression analysis. The Cox Snell residual test was used to evaluate the model’s goodness of fit. The Crude Hazard Ratio (CHR) and Adjusted Hazard Ratio (AHR) were used to assess the strength of association between the independent and dependent variables. Overall, a P-value ≤ 0.05 was considered statistically significant, with their respective 95% confidence intervals. Ethical clearance was obtained from the University of Gondar, Institute of Public Health Ethical Review Committee (Ref No/IPH/1504/2013 E.C.). The members of the ethical review Committee wrote letters of cooperation to UOGCSH, and permission was later obtained from the clinical director, department head and card room head at the hospital. The patients will not be harmed because the study was carried out using appropriate information from their medical charts. The name or any other identifying information was not recorded on the checklist, and all information obtained from the chart was strictly confidential and kept in a secure location. Following these approvals, access to the medical charts was granted, and we did our best to maintain participant confidentiality by storing them in a file cabinet and keeping them in a keyed and locked.