Pattern of disease and determinants of mortality among ICU patients on mechanical ventilator in Sub-Saharan Africa: a multilevel analysis

This Multi-center prospective cohort study was conducted in Southern Ethiopia teaching referral hospital ICUs from October 2020 to November 2022. Three teaching and referral hospital ICUs were selected randomly from ten teaching hospitals in Southern Ethiopia, namely Hawassa university referral hospital (HURH), Dilla University referral hospital (DURH), and Wolaita Sodo referral hospital (WURH). The ICUs are providing a similar level of care with almost similar staff profiles, monitoring modalities, ICU infrastructure, medical supplies, and admission patterns.

All patients over the age of 12 who received mechanical ventilation at DURH, Wolaita Sodo University Hospital, and Hawassa University Specialized Hospital ICUs and stayed there for longer than 24 h of admission were included; whereas, patients receiving non-invasive oxygen supplementation, patients who were discharged, transferred to, or left against medical advice before 24 h of mechanical ventilation, patients who were admitted to ICU on re-admission, and patients without attendants were excluded.

The cumulative incidence of 28-day mortality, duration of a mechanical ventilator, incidence of complications, pattern of disease, and length of stay in ICU were dependent variables, while socio-demographic characteristics: Age, sex, height, weight, BMI; Admission characteristics: date of ICU admission, date of discharge, vital sign, diagnosis, admission category, time of admission, laboratory index, disease severity score; Mechanical ventilation parameters: date of initiation, initial mode, date of tracheostomy, and date of weaning were independent variables.

The data were collected prospectively from three hospital ICUs patients in whom mechanical ventilation was initiated. After 2 h of mechanical ventilation, trained ICU nurses followed eligible participants for 28 days. The data were collected with a validated questionnaire and tools adopted from previous studies [11, 21‐23]. A total of 630 patients were recruited consecutively from each ICU from October 2020 to November 2022 as per proportion allocation to size (Fig. 1).

The data collection included Socio-demographic characteristics: Age, Sex, Height, Weight, BMI); Admission characteristics: date of ICU admission, date of discharge, vital signs, diagnosis, admission category, time of admission, laboratory index, disease severity score; Complications: nosocomial infection, ventilator-associated pneumonia, sepsis, ARDS, aspiration, unplanned extubation, endotracheal tube blockage, tracheostomy loose/stenosis/fistula, cardiac arrest, acute kidney injury, bedsore); comorbidities: hypertension, diabetes mellitus, cardiovascular disease, Asthma/COPD, liver failure, renal failure, others, and clinical outcomes including mortality, duration of a mechanical ventilator, and complications were measured for 28 days. Furthermore, the disease severity scores including sequential organ assessment (SOFA) score, modified SOFA score, Acute Physiology, and Chronic Health Evaluation (APACHE) II score, and modified APACHE II score were recorded at admission and thereafter till discharge every seven days to compare the predicting ability and 28-day mortality of these tools.

The data were checked, coded, and entered into Epi-info version 7.0 and imported to SPSS version 22 for analysis. However, STATA version 14 was used to produce some graphs, multilevel analysis and pool the incidence of clinical outcomes. Descriptive statistics were summarized with tables and figures. Categorical variables were reported in frequency and percentage. The numerical data were reported in mean ± SD for symmetric and median (Interquartile range) for asymmetric numeric data. The outlier of the data was checked with standardized residuals while Shapiro–Wilk tests were employed for the normality test. The multicollinearity among independent variables was checked by the Variance inflation factor (VIF), and a VIF of greater than 10 was considered multicollinearity. The association of demographic characteristics, admission category, indications for MV, and complications of MV with mortality were analyzed using multilevel binary logistic regression as there was clustering as depicted with an intraclass correlation coefficient (ICC = 8.5%). All variables showing significance on multilevel bivariate analysis at a p-value less than 0.25 will be taken to multilevel multivariate analysis. In multivariate analysis, a p-value of less than 0.05 was considered for the statistical association. This study was conducted in compliance with Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational studies [24].

The primary endpoints were the incidence of 28-day mortality and duration of mechanical ventilation, while the incidence of complications during MV was the secondary clinical outcome.

Definition incidence of clinical outcomes refers to the occurrence of new clinical events including mortality, morbidity, and prolonged mechanical ventilation in 28 days of ICU stay. Morbidities are defined as any complication developed during Mechanical ventilation which includes nosocomial infection, ventilator-associated pneumonia, sepsis, ARDS, aspiration, unplanned extubation, endotracheal tube blockage, tracheostomy loose/stenosis/fistula, cardiac arrest, acute kidney injury, and bedsore. Prolonged mechanical ventilation refers to the duration of mechanical ventilation for over 21 days [25, 26].

The hierarchical nature of the hospital ICU care in which patients are nested within clusters, a multivariable multilevel logistic regression analysis was employed to account for this clustering effect. Thence, four models containing variables of interest were fitted for this study as follows: Model I (Null model) was fitted without explanatory variables to test random variability in the intercept and to estimate the intraclass correlation coefficient (ICC), Model II assessed the effects of individual-level predictors, Model III assessed the effects of hospital-level predictors, and Model IV (Full model) examined the effects of both individual and hospital-level characteristics simultaneously. The Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to select the model, and the model with low AIC and BIC values was considered the best-fitted model. Based on AIC and BIC, the full (model with individual and hospital-related variables) model had the smallest AIC and BIC value. Therefore, the full model best fits the data.

This study was reviewed and approved by Dilla University, College of Health Science and Medicine Research Ethics and Review Board (RERB), and a Unique Identification Number (UIN- duirb/001/21-08) was received. Besides, the protocol was registered retrospectively in ClinicalTrials.gov (NCT05303831) on March 30, 2022. The study was conducted in compliance with the Helsinki declaration for human and animal studies [27]. Informed consent was received from all participants’ legal attendants or guardians, and all patient identifiers were kept anonymous at all times. Besides, a formal letter was written to each University hospital ICU director to get permission.

Out of 648 eligible cohorts, 630 (97.25) completed the 28-day prospective follow-up. The mean (standard deviation) age of the participants was 38.9 ± 17.6 years, where more than two-thirds of them were in the age ranges of 19 to 29 and > 40 years. The majority of participants were male, 426 (67.7%) (Table 1).

This study showed that acute respiratory syndrome (ARDS) was the commonest diagnosis 178 (28.3%), followed by postoperative events 113 (17.9%), traumatic brain injury (TBI) 92 (14.6%), and stroke 80 (12.7%). The main reason for invasive mechanical ventilation was found to be respiratory failure 393 (62.4%) followed by airway protection 95 (15.1), and postoperative respiratory support 89 (14.1) (Additional file 2: Table S1).

Of the 630 patients, medical cases accounted for more than half 356 (56.2) followed by trauma 125 (19.8%), and surgical cases, 89 (14.1%). The majority of patients were admitted to the ICU during day time and nighttime. Overall, 496 (78.7%) of the patients had comorbidities, where hypertension was the highest 151 (245) followed by respiratory (Asthma and COPD) 122 (19.4%), and DM 72 (11.4%). However, in a few patients, 42 (6.7%) had more than one comorbidity, while only 134 (21.2%) had unconfirmed comorbidities (Table 2).

The majority of patients 456 (71.7%) sustained at least one complication which included ventilator-associated pneumonia (VAP), cardiac arrest, shock, acute kidney injury (AKI), and others, where VAP alone accounted for 198 (31.4%) of the complications. The disease severity scores including Acute Physiologic and Health Evaluation II (APACHE II), Modified Acute Physiologic and Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and Modified Sequential Organ Failure Assessment (MSOFA) were employed during admission of patients, and MAPACHE II) showed a significant association with 28-day survival status with strong predicting probability (Table 2).

The incidence of 28-day mortality among ICU patients on a mechanical ventilator was 49% (95% CI: 36–58). The overall pooled prevalence of comorbidity among non-survivors was 47% (95% CI: 36–58) (Fig. 2). The histogram and normal distribution curve showed that the majority of patients were on mechanical ventilation for the first fifteen days (Additional file 1: Fig. S1). The duration of MV and baseline Glasgow Coma Scale did not show a significant difference in survival status as depicted on a line graph with error bars (Figs. 3 and 4).

The mode of ventilation in the intensive care unit depends on the admission diagnosis, the severity of the disease, and the presence of other comorbidities. For example, patients with acute respiratory disease distress syndrome benefit from ArdsNet ventilator protocol which includes low tidal volume (4–8 ml/kg) predicted body weight, peak inspiratory pressure < 35 cmH20, and titrated FIO2 as per patient progress while patients with COPD require high peak flow rate with slow respiratory rate. This study tried to investigate the effects of ventilator parameters, duration of mechanical ventilation, and admission disease severity score on survival status and found a significant standardized mean difference in most of the parameters (Fig. 5).

Several prognostic scores have been employed in the intensive care unit, which includes APACHE I-1 V, SOFA, and biomedical tests. However, the parameters used inquired are blood gas analysis and blood chemistry analysis which is not feasible in a resource-limited setting. In this regard, many studies have been conducted to compare the sensitivity, specificity, and predictability of modified tests that are applicable in a resource-limited environment, but the findings are yet inconsistent. This study was intended to compare the predictability of modified SOFA and modified APACHE II scores. The receiver operating curve showed the area under the curve for APACHE II, modified APACHE II, SOFA, and modified SOFA, revealing a similar prediction of 28-day mortality (Fig. 6 and Additional file 3: Table S2).

This multilevel multivariate analysis revealed that patients with medical causes of ICU admission were approximately 3 times (AOR = 2.9; 95% CI: 1.49–5.49, P value = 0.002) more likely to die compared to surgical (AOR = 1.3; 95% CI: 0.37–2.36), trauma (AOR = 0.9; 95% CI: 0.43–2.13), and other causes of admission (Additional file 4: Table S3 and 3).

This is the first-ever multicenter prospective cohort study investigating mortality and determinants among patients on mechanical ventilation in sub-Saharan Africa. Besides, the study has taken into account the clustering effect of service variation by conducting a multilevel analysis. However, the study has certain limitations including a lack of arterial and blood chemistry tests to calculate disease severity scores in some patients, and the heterogeneity of cases as the ICUs were general hospitals that admitted a mix of cases.

This study revealed that the mortality of patients receiving mechanical ventilation in ICU was very high compared to previous studies conducted in western countries, which is strongly associated with the indication for mechanical ventilation, time of admission, baseline vital signs at admission, presence of comorbidities, and complications of mechanical ventilation. This entails mitigating strategies including but not limited to the provision of skilled medical staff, adequate medical supplies, adequate monitoring, and portable diagnostic modalities.

This is a multicenter prospective cohort study which is the first of its kind in Sub-Saharan Africa with multilevel analysis, but further multicenter prospective studies with a long period of follow-up might be inquired in specialized ICU setups of the homogenous population to investigate the certain impacts of the prolonged mechanical ventilator on patient outcomes.