Introduction
The transport of critically ill patients is an essential component of emergency and critical care medicine. It may be required for diagnostic procedures, access to therapeutic interventions unavailable in the referring facility, or transfer to a hospital with specialized resources. However, moving an unstable patient outside the controlled environment of the intensive care unit exposes them to physiological, technical, and organizational challenges that may compromise clinical stability. International guidelines, therefore, emphasize the importance of careful preparation, appropriate monitoring, adequate equipment, and appropriately trained personnel to ensure the safety of intra- and interhospital transport [1][2]. Adverse events associated with the transport of critically ill patients include respiratory and hemodynamic disturbances, cardiac arrhythmias, incidents involving invasive devices, and technical failures. A review of the literature reported that complications may occur in a substantial proportion of transports, including changes in heart rate, hypotensive or hypoxemic episodes, and equipment-related incidents [3]. More recently, a systematic review and meta-analysis of 12,313 intrahospital transports estimated a pooled adverse-event rate of 26.2%, with substantial heterogeneity across studies [4]. Life-threatening events were considerably less frequent, with a pooled incidence of 1.47% [4].
The occurrence of these events depends on patient characteristics, disease severity, transport conditions, and the organization of care. Mechanical ventilation, specific ventilatory parameters, and the severity of the underlying condition have been identified as factors associated with adverse events [5]. Transport duration, monitoring conditions, equipment availability, and the qualifications of accompanying personnel may also influence transport safety. These factors support the implementation of standardized procedures addressing patient preparation, communication between teams, equipment selection, monitoring, and transport documentation [1][2][6]. Improving transport safety also relies on the implementation of protocols and checklists. Current recommendations emphasize assessment of the risk-benefit ratio before transport, prior stabilization of the patient, verification of the airway and invasive devices, and availability of the medications and equipment required to manage potential clinical deterioration [2][6]. More recent studies have shown that the use of dedicated transport checklists can improve compliance with safety recommendations [7].
In low-resource settings, these requirements may be difficult to meet because of constraints related to infrastructure, availability of adequately equipped ambulances, monitoring equipment, equipment maintenance, and specialized human resources. These challenges may be particularly significant in large urban areas, where interhospital transfers are frequent and subject to additional logistical constraints. Nevertheless, data on the safety of critically ill patient transport in sub-Saharan Africa, and particularly in the Democratic Republic of the Congo, remain limited. In Kinshasa, the transfer of critically ill patients between healthcare facilities or within hospitals is an important component of emergency care and the management of patients requiring specialized services. In this context, the lack of local data on the frequency and nature of transport-related complications, as well as on the factors associated with their occurrence, limits the ability to identify major weaknesses in the transport system and to develop context-specific interventions. The present study aimed to assess the safety of critically ill patient transport in Kinshasa, determine the frequency and nature of complications occurring during transport, and identify factors associated with their occurrence. It also sought to provide evidence to support improvements in clinical practices, staff training, and the organization of critically ill patient transport in the local context.
Methods
Study design and setting
This was an analytical cross-sectional study conducted to assess the safety of intra- and interhospital transport of critically ill patients in Kinshasa, Democratic Republic of the Congo. The study was conducted in three referral healthcare facilities in Kinshasa: Hôpital Central de la Police, Centre Médical Diamant, and Médecin de Nuit. These facilities were selected because of their substantial emergency and critical care activity and their frequent use of intra- and interhospital patient transport. The study period extended from September 2023 to December 2025.
Study population and eligibility criteria
The study population consisted of adult patients admitted to emergency departments or intensive care units who underwent intra- or interhospital transport during the study period.
Patients were eligible if they met all of the following criteria:
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Age ≥18 years;
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Admission to an emergency department or intensive care unit.
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Completion of an intra- or interhospital transport;
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Availability of a transport monitoring form documenting the transfer; and
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Availability of sufficient clinical and logistical information for analysis.
Patients transferred directly from the operating room to a conventional inpatient ward without admission to an intensive care unit were excluded. Records with insufficient or non-exploitable information were also excluded. A total of 130 patients fulfilled the eligibility criteria and were included in the final analysis.
Sampling and sample size
A non-probability consecutive sampling approach was used. All eligible patients for whom a complete and exploitable transport record was available during the study period were included. The final sample consisted of 130 patients. No formal a priori sample size calculation was performed; the sample size was determined by the number of eligible and adequately documented patients available during the study period.
Data sources and data collection
Data were collected using a standardized data collection form developed for the study. Information was obtained from transport monitoring forms, patient medical records, and transport team reports. Data collection was performed by anesthesia and critical care assistants, general practitioners, nurses, and anesthesia technicians who had been trained by the investigator before the start of data collection. The completed forms were reviewed by the investigator for completeness and consistency. Records with insufficient or unusable information were excluded from the final analysis. The data collection form included information on patient characteristics, clinical status, transport characteristics, accompanying personnel, monitoring, transport-related adverse events, and patient outcomes.
Study variables
Primary outcome
The primary outcome was the occurrence of at least one transport-related adverse event during transport or immediately after arrival at the destination. A transport-related adverse event was defined as any clinically relevant physiological deterioration or incident occurring during transport or immediately after arrival, including respiratory, hemodynamic, behavioral, device-related, or technical events. Patients could experience more than one adverse event during the same transport episode. The overall adverse-event rate was therefore defined as the proportion of patients who experienced at least one adverse event, whereas individual event frequencies were reported separately.
Secondary outcomes
Secondary outcomes included specific transport-related adverse events, including
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Respiratory events, including desaturation and ventilator dyssynchrony.
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Hemodynamic events, including hypotension and tachycardia;
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Agitation;
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Vascular access or catheter dislodgement;
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Accidental extubation.
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Cardiac arrest.
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Technical failures;
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Adverse events requiring rescue intervention; and
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Transport-associated death.
Explanatory variables
The explanatory variables included patient-related and transport-related characteristics. Patient-related variables included age, sex, hypertension, diabetes mellitus, traumatic brain injury, other comorbidities, and vital signs before and during transport. Transport-related variables included intra- or interhospital transport, transport duration, distance travelled, type of vehicle, and professional category of accompanying personnel. Organizational characteristics included other available features of the transport organization. Technical failures were recorded as transport-related adverse events and were therefore not considered independent explanatory variables in the multivariable analysis.
Operational definitions
The following operational definitions were used:
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Desaturation: oxygen saturation <90%.
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Hypotension: systolic blood pressure <90 mmHg.
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Tachycardia: heart rate >100 beats/minute.
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Bradycardia: heart rate <60 beats/minute.
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Agitation: excessive and uncontrolled motor or verbal activity occurring during transport.
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Ventilator dyssynchrony: inappropriate coordination between the patient's respiratory effort and mechanical ventilation.
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Accidental extubation: unplanned displacement or removal of the endotracheal tube from the trachea.
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Cardiac arrest: cessation of effective cardiac mechanical activity requiring resuscitation.
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Technical failure: accidental interruption or malfunction of a medical device required for patient monitoring or management during transport.
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Intrahospital transport: movement of a patient between different locations within the same healthcare facility.
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Interhospital transport: transfer of a patient from one healthcare facility to another.
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Transport duration: time elapsed between departure from the referring location and arrival at the destination.
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Adverse event requiring rescue intervention: an adverse event requiring an additional therapeutic intervention to stabilize the patient, such as intubation or reintubation, initiation of vasopressor support, emergency vascular access, or another urgent rescue intervention.
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Transport-associated death: death occurring during transport or immediately after arrival at the destination and temporally associated with the transport episode.
Assessment of transport characteristics
For each transport episode, the type of transport, duration, distance traveled, vehicle used, accompanying personnel, and occurrence of adverse events were recorded. Transport vehicles were classified as either medicalized ambulances or non-medicalized vehicles. The accompanying personnel were categorized according to their professional qualification, including anesthesia and critical care assistants, general practitioners, nurses, and anesthesia technicians. No transport was accompanied by an anesthesiologist-intensivist or emergency physician.
Statistical analysis
Data were entered, coded, and analyzed using IBM SPSS Statistics version 22.0. Categorical variables were summarized using absolute frequencies and percentages. Continuous variables were summarized using means and standard deviations when approximately normally distributed. The overall frequency of transport-related adverse events was calculated as the proportion of patients who experienced at least one adverse event. For bivariate analyses, categorical variables were compared using the Pearson chi-square test or Fisher's exact test when expected cell counts were small. Continuous variables were compared using an appropriate parametric or non-parametric test according to the distribution of the data. Associations between explanatory variables and the primary outcome were initially assessed using univariable analyses. A multivariable binary logistic regression model was used to identify factors that were independently associated with the occurrence of at least one transport-related adverse event. Candidate variables were selected based on clinical relevance, biological plausibility, and their potential contribution to transport-related risk. Because only 33 patients experienced at least one adverse event, the number of variables included in the final model was deliberately restricted to minimize the risk of overfitting. Potential collinearity between transport-related variables was assessed before model fitting. Technical failures were not included as explanatory variables in the multivariable model because they were included in the composite definition of transport-related adverse events. Associations were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided p-value <0.05 was considered statistically significant.
Missing data
Patients with insufficient information to determine the occurrence of the primary outcome or to assess the main transport characteristics were excluded from the final analysis. For variables with missing values among included patients, analyses were performed using the available observations. The number of observations included in each analysis was reported where applicable.
Assessment of potential bias
Several measures were taken to minimize potential sources of bias. A standardized data collection form was used across the three participating healthcare facilities, and all data collectors received training before data collection. Selection bias may have resulted from the exclusion of incomplete or non-exploitable records. Information bias was also possible because some transports did not benefit from continuous physiological monitoring, potentially leading to under-recognition of transient adverse events. The multicenter design was intended to reduce the influence of practices specific to a single healthcare facility.
Ethical considerations
The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Authorization to conduct the study was obtained from the medical administration of the three participating healthcare facilities. The study protocol was approved by the Ethics Committee of the Kinshasa School of Public Health. Patient anonymity and confidentiality were maintained throughout data collection, data management, analysis, and reporting. The collected information was used exclusively for scientific purposes.
Results
Sociodemographic and clinical characteristics of the study population
Table 1 shows the sociodemographic and clinical characteristics of the 130 patients included in the final analysis. The study population was predominantly male (78/130, 60.0%). The mean age was 36.2 ± 12.0 years, with ages ranging from 18 to 72 years. The most represented age group was 31–40 years (38 patients, 29.2%), followed by the 21–30-year age group (32 patients, 24.6%). Hypertension was the most frequently reported comorbidity (29 patients, 22.3%), followed by diabetes mellitus (20 patients, 15.4%). Traumatic brain injury was reported in 16 patients (12.3%), while other comorbid conditions were reported in 13 patients (10.0%).
| Characteristics | n | % |
| Sex | ||
| Male | 78 | 60.0 |
| Female | 52 | 40.0 |
| Age, years | ||
| Mean ± SD | 36.2 ± 12.0 | — |
| Range | 18–72 | — |
| Age groups, years | ||
| ≤20 | 18 | 13.8 |
| 21–30 | 32 | 24.6 |
| 31–40 | 38 | 29.2 |
| 41–50 | 25 | 19.2 |
| >50 | 17 | 13.2 |
| Clinical conditions/comorbidities | ||
| Hypertension | 29 | 22.3 |
| Diabetes mellitus | 20 | 15.4 |
| Traumatic brain injury | 16 | 12.3 |
| Other conditions | 13 | 10.0 |
Characteristics of transport episodes
Table 2 presents the characteristics of the 130 transport episodes analyzed. Among these, 82 (63.1%) were interhospital transfers and 48 (36.9%) were intrahospital transfers. The mean transport duration was 34 ± 12 minutes, with a mean distance travelled of 15 ± 6 km. A medicalized ambulance was used for 92 transports (70.8%), whereas 38 transports (29.2%) were performed using a non-medicalized vehicle. Nurses were the most frequently involved accompanying personnel (42, 32.3%), followed by senior anesthesia assistants (33, 25.4%), anesthesia technicians (32, 24.6%), and general practitioners (23, 17.7%). No transport was accompanied by an anesthesiologist-intensivist or emergency physician.
| Characteristics | n | % |
| Type of transport | ||
| Intrahospital | 48 | 36.9 |
| Interhospital | 82 | 63.1 |
| Transport duration, min | ||
| Mean ± SD | 34 ± 12 | — |
| Distance travelled, km | ||
| Mean ± SD | 15 ± 6 | — |
| Mode of transport | ||
| Medicalized ambulance | 92 | 70.8 |
| Non-medicalized vehicle | 38 | 29.2 |
| Accompanying personnel | ||
| Senior anesthesia assistant | 33 | 25.4 |
| General practitioner | 23 | 17.7 |
| Nurse | 42 | 32.3 |
| Anesthesia technician | 32 | 24.6 |
| Anesthesiologist-intensivist or emergency physician | 0 | 0.0 |
Transport-related adverse events
Table 3 summarizes the clinical and technical adverse events recorded during transport episodes. Overall, 33 patients (25.4%) experienced at least one transport-related adverse event, whereas 97 (74.6%) did not. Agitation was the most frequently reported event, occurring in 19 patients (14.6%), followed by desaturation below 90% in 18 patients (13.8%) and hypotension in 15 patients (11.5%). Ventilator dyssynchrony occurred in 13 patients (10.0%), while tachycardia occurred in 12 patients (9.2%). Device-related events included accidental removal of an intravenous line or catheter in 10 patients (7.7%) and accidental extubation in 2 patients (1.5%). Eight patients (6.2%) reported technical failures, such as problems with equipment and a loss of oxygen supply. One patient (0.8%) experienced cardiac arrest during transport. Because individual patients could experience more than one adverse event during the same transport episode, the frequencies of individual events were not mutually exclusive.
| Adverse event | n | % |
| Agitation | 19 | 14.6 |
| Desaturation <90% | 18 | 13.8 |
| Hypotension <90 mmHg | 15 | 11.5 |
| Ventilator dyssynchrony | 13 | 10.0 |
| Tachycardia >100 beats/min | 12 | 9.2 |
| Accidental removal of IV line/catheter | 10 | 7.7 |
| Technical failure | 8 | 6.2 |
| Accidental extubation | 2 | 1.5 |
| Cardiac arrest | 1 | 0.8 |
Patient outcomes following transport
Table 4 presents patient outcomes following the transport episode. At the end of transport, 112 patients (86.2%) were classified as stable. Fifteen patients (11.5%) experienced adverse events requiring rescue interventions, while 3 patients (2.3%) experienced transport-associated death.
| Outcome | n | % |
| Stable after transport | 112 | 86.2 |
| Adverse events requiring rescue intervention | 15 | 11.5 |
| Transport-associated death | 3 | 2.3 |
Bivariate analysis of factors associated with transport-related adverse events
Table 5 presents the bivariate analysis of factors associated with transport-related adverse events. Overall, 33 patients (25.4%) experienced at least one transport-related adverse event, whereas 97 (74.6%) did not. Factors significantly associated with transport-related adverse events included hypertension, transport duration greater than 30 minutes, use of a non-medicalized vehicle, absence of specialized physician supervision, and technical failures. In bivariate analysis, age over 40 years and interhospital transport did not significantly correlate with the outcome. Among patients who experienced adverse events, 15 (45.5%) were over 40 years old, compared to 27 (27.8%) among those without adverse events (p = 0.062). Hypertension was more frequent among patients with adverse events than among those without adverse events (36.4% vs. 17.5%; p = 0.025). Interhospital transport was also more frequent among patients with adverse events (75.8% vs. 58.8%), although the association was not statistically significant (p = 0.081). Transport duration >30 minutes was associated with a higher frequency of adverse events (54.5% vs. 29.9%; p = 0.011). The use of a non-medicalized vehicle showed the strongest bivariate association, with such vehicles being used in 84.8% of patients with adverse events compared with 10.3% of those without adverse events (p <0.001). Similarly, adverse events were more frequent in the absence of specialized physician supervision (72.7% vs. 46.4%; p = 0.009). Technical failures were also significantly associated with adverse events (15.2% vs. 3.1%; p = 0.025). Sex, diabetes mellitus, and traumatic brain injury were not significantly associated with transport-related adverse events.
| Variables | Adverse events (n = 33), n (%) | No adverse events (n = 97), n (%) | p-value |
| Age >40 years | 15 (45.5) | 27 (27.8) | 0.062 |
| Male sex | 21 (63.6) | 57 (58.8) | 0.622 |
| Hypertension | 12 (36.4) | 17 (17.5) | 0.025 |
| Diabetes mellitus | 7 (21.2) | 13 (13.4) | 0.287 |
| Traumatic brain injury | 6 (18.2) | 10 (10.3) | 0.241 |
| Interhospital transport | 25 (75.8) | 57 (58.8) | 0.081 |
| Transport duration >30 min | 18 (54.5) | 29 (29.9) | 0.011 |
| Non-medicalized vehicle | 28 (84.8) | 10 (10.3) | <0.001 |
| Absence of specialized physician supervision | 24 (72.7) | 45 (46.4) | 0.009 |
| Technical failure | 5 (15.2) | 3 (3.1) | 0.025 |
Multivariable analysis of factors associated with transport-related adverse events
Table 6 presents the multivariable logistic regression analysis of factors associated with transport-related adverse events. Age >40 years, hypertension, interhospital transport, transport duration >30 minutes, use of a non-medicalized vehicle, and the absence of specialized physician supervision were independently associated with transport-related adverse events. The strongest association was observed with the use of a non-medicalized vehicle (adjusted OR [aOR] = 3.5; 95% CI: 1.6–7.4; p = 0.001), followed by the absence of specialized physician supervision (aOR = 2.8; 95% CI: 1.3–6.0; p = 0.005) and interhospital transport (aOR = 2.7; 95% CI: 1.3–5.6; p = 0.004). Hypertension (aOR = 2.5; 95% CI: 1.2–5.1; p = 0.01), transport duration >30 minutes (aOR = 2.4; 95% CI: 1.1–5.0; p = 0.02), and age >40 years (aOR = 2.1; 95% CI: 1.1–4.0; p = 0.03) were also independently associated with transport-related adverse events.Male sex, diabetes mellitus, and traumatic brain injury were not independently associated with the outcome.
| Variable | Adjusted OR | 95% CI | p-value |
| Age >40 years | 2.1 | 1.1–4.0 | 0.03 |
| Male sex | 1.2 | 0.6–2.3 | 0.45 |
| Hypertension | 2.5 | 1.2–5.1 | 0.01 |
| Diabetes mellitus | 1.9 | 0.9–3.8 | 0.07 |
| Traumatic brain injury | 1.8 | 0.8–3.9 | 0.09 |
| Interhospital transport | 2.7 | 1.3–5.6 | 0.004 |
| Transport duration >30 min | 2.4 | 1.1–5.0 | 0.02 |
| Non-medicalized vehicle | 3.5 | 1.6–7.4 | 0.001 |
| Absence of specialized physician supervision | 2.8 | 1.3–6.0 | 0.005 |
Abbreviations: OR, odds ratio; CI, confidence interval.
Discussion
Principal findings
This study assessed the safety of intra- and interhospital transport of critically ill patients in three healthcare facilities in Kinshasa. As shown in Table 3, 33 of 130 patients (25.4%) experienced at least one transport-related adverse event. The most frequently recorded events were agitation, desaturation, hypotension, ventilator dyssynchrony, and tachycardia. Three patients (2.3%) experienced transport-associated death.
Table 6 shows that age >40 years, hypertension, interhospital transport, transport duration >30 minutes, and the use of a non-medicalized vehicle were independently associated with transport-related adverse events. The strongest association was observed with the use of a non-medicalized vehicle (aOR = 3.5; 95% CI: 1.6–7.4; p = 0.001).
Frequency and nature of adverse events
The 25.4% frequency observed in this study is comparable to that reported in a systematic review and meta-analysis of intrahospital transport, which estimated a pooled adverse-event frequency of 26.2% [8]. However, comparisons should be made cautiously because our study included both intra- and interhospital transport and used a broad definition encompassing physiological, device-related, and technical events.
Respiratory and hemodynamic disturbances were prominent in our cohort. Desaturation occurred in 13.8% of patients and hypotension in 11.5%. These findings are consistent with previous studies identifying respiratory and cardiovascular instability among the most frequent complications during the transport of critically ill patients [9][11]. Accidental extubation occurred in 1.5% of patients, while one patient experienced cardiac arrest, indicating the possible severity of transport-related deterioration
Technical failures occurred in 6.2% of transports and were significantly associated with adverse events in bivariate analysis. However, they were excluded from the multivariable model because technical failures were part of the primary outcome's composite definition. This approach avoids circularity in the interpretation of the adjusted analysis.
Factors associated with adverse events
As shown in Table 5, hypertension, transport duration >30 minutes, use of a non-medicalized vehicle, absence of specialized physician supervision, and technical failures were significantly associated with adverse events in bivariate analysis. Age >40 years and interhospital transport were not statistically significant in bivariate analysis but became independently associated with the outcome after adjustment.
The association between interhospital transport and adverse events is clinically plausible because such transfers generally involve greater logistical complexity, longer distances, and coordination between different healthcare facilities [14]. Similarly, transport duration >30 minutes may increase exposure to physiological deterioration, equipment problems, and delays in intervention [10][15].
The strongest association was observed with non-medicalized vehicles. These vehicles may lack continuous monitoring, adequate oxygen delivery, suction, emergency medications, and other equipment required for critically ill patients. This finding supports the need for minimum equipment standards and appropriate transport platforms for high-risk patients.
The multivariable association involving absence of specialized physician supervision should, however, be interpreted cautiously because no transport in this cohort was accompanied by an anesthesiologist-intensivist or emergency physician. Therefore, this variable does not represent a true comparison between transports with and without specialist supervision, and no causal inference can be made from this finding.
Clinical and organizational implications
The findings suggest that improving transport safety in Kinshasa requires attention to both patient-related and system-level factors. Standardized pre-transport assessment, patient stabilization, equipment checklists, adequate oxygen and medication supplies, secure airway and vascular access, appropriate monitoring, and clear communication between referring and receiving teams should be prioritized [10][15][17].
In a resource-constrained setting, these measures could provide an important safety improvement even before the implementation of fully specialized critical care transport systems.
Strengths and limitations
The main strength of this study is that it provides multicenter data on critically ill patient transport in Kinshasa, where published evidence remains limited. The study also assessed both clinical and organizational factors associated with adverse events.
The study has several limitations. The small sample size and limited number of adverse events reduce statistical power and may result in imprecise adjusted estimates. The multivariable findings should therefore be considered exploratory. The observational design prevents causal inference, and residual confounding by illness severity cannot be excluded. Some adverse events may also have been missed because continuous physiological monitoring was not available during all transports. Finally, the inclusion of only three healthcare facilities limits generalizability.
Conclusion of the discussion
This study found that transport-related adverse events occurred in one quarter of critically ill patients transported in three healthcare facilities in Kinshasa. Respiratory and hemodynamic disturbances were frequent, while the use of non-medicalized vehicles, longer transport duration, interhospital transfer, age >40 years, and hypertension were independently associated with adverse events.
These findings support the development of standardized critical care transport procedures, minimum equipment requirements, staff training, and appropriate patient monitoring. Larger prospective multicenter studies are needed to confirm these associations and develop context-specific strategies to improve the safety of critically ill patient transport in the Democratic Republic of the Congo.
Declarations
Conflict of Interest
The authors declare that they have no conflicts of interest.
Author Contributions
HM conceived the study and contributed to study design, data collection, data analysis, and manuscript preparation. AMB contributed to the study design, data analysis, interpretation of results, and critical revision of the manuscript. FM, RM, AY, JPM, SM, JDK, AM, DM, TM, WM, MB, and BB contributed to data collection and interpretation of the findings and critically reviewed the manuscript. All authors contributed to the final manuscript, approved the submitted version, and agreed to be accountable for all aspects of the work.
Funding
This study received no specific funding from any public, commercial, or not-for-profit funding agency.
Acknowledgments
The authors thank the medical and nursing staff of the participating healthcare facilities for their contribution to patient care and data collection.
Consent for Publication
Not applicable. This study used routinely collected clinical and transport-related data and did not involve the publication of identifiable individual patient information.
Data Availability
The datasets generated and analyzed during the current study are not publicly available because they contain potentially identifiable clinical information but may be available from the corresponding author on reasonable request, subject to applicable ethical and institutional requirements.