OBJECTIVES: Managing severe pneumonia remains a challenge. Rapid diagnostic tests, such as multiplex polymerase chain reaction (mPCR), facilitate quick microorganism identification and may enable timely and appropriate antimicrobial therapy. However, studies from low-income countries are scarce. This study aimed to evaluate the diagnostic characteristics of mPCR and its impact on antibiotic therapy and outcomes in critically ill patients with pneumonia. DESIGN: Multicenter observational study. SETTING: Twelve ICUs across Morocco. PATIENTS: Adult patients with pneumonia requiring invasive mechanical ventilation, including community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP), and ventilator-associated pneumonia (VAP). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Respiratory samples were analyzed using both mPCR and conventional microbiological methods. The diagnostic performance of mPCR was evaluated, including its sensitivity and specificity. Additionally, the appropriateness of mPCR-induced modifications in empiric antibiotic therapy and their impact on patient outcomes were assessed. A total of 210 patients were included, with a median age of 50 years (range, 33–67 yr), of whom 66.2% were male. Pneumonia types were distributed as 30% CAP, 58% VAP, and 12% HAP. mPCR demonstrated a sensitivity of 96.9% (95% CI, 92.3–99.2%) and a specificity of 92% (95% CI, 91–93%). Following mPCR, antibiotic therapy modifications were observed in 58% of patients (n = 122), including de-escalation or cessation in 11% (n = 23), escalation in 26.5% (n = 56), adequacy adjustments in 7.5% (n = 16), and initiation of antibiotics in 13% (n = 27). The appropriateness of antibiotic therapy increased significantly from 38.7% (n = 83) to 67% (n = 141; difference, 27.5%; 95% CI, 18.3–36.7; p < 0.0001). Generalized mixed model analysis revealed that appropriate post-mPCR antibiotic therapy was associated with reduced mortality (adjusted odds ratio, 0.37; 95% CI, 0.15–0.93; p = 0.038). CONCLUSIONS: Our findings suggest that the use of mPCR is associated with a significant improvement in the appropriateness of empiric antibiotic therapy and is also associated with a positive impact on the outcome of patients with pneumonia.

Introduction: Sleep disturbances are a common and often underestimated complication during intensive care unit (ICU) stays. These disturbances can significantly impact patients’ recovery and overall well-being. This study aimed to assess the sleep quality of ICU patients and investigate the environmental and clinical factors that affect sleep quality during their ICU stay. Methods: We conducted a six-month cross-sectional study involving patients who had stayed in the ICU for at least three nights and were oriented to time and place upon discharge. Sleep quality was assessed using the Arabic version of the Freedman Sleep Questionnaire. Both environmental factors (e.g., noise, light, and nursing interventions) and clinical variables (illness severity and pain) were examined. The differences across three time periods were analyzed using the Wilcoxon test and Spearman’s correlation. Multiple regression analysis identified the factors influencing sleep quality. Statistical analyses were performed using JAMOVI software (version 2.3.28). Results: The study enrolled 328 patients, with an average age of 49.74 ± 17.89 years. Of the participants, 75.3% were adults. The primary reasons for admission were circulatory distress (45.73%) and metabolic disorders (24.09%). Sleep quality was significantly lower in the ICU compared to patients’ sleep at home (Z = −14.870, p < 0.001). The EVA and APACHE II scores had a statistically significant effect on sleep quality (p < 0.001 and p = 0.015, respectively). In contrast, the Charlson and Quick SOFA scores did not show significant effects (p = 0.128 and p = 0.894). Environmental factors, including noise (p = 0.008), light (p = 0.009), and nursing interventions (p = 0.009), significantly impacted sleep quality. Conclusions: Patients in the ICU generally reported poor sleep quality. Our findings suggest that improving pain management, minimizing environmental noise, and reducing staff-related disturbances could significantly enhance sleep quality for patients in the intensive care unit (ICU).

Introduction: For patients in intensive care units, the Richards–Campbell Sleep Questionnaire (RCSQ) seems to be a useful tool for assessing sleep quality. However, its application in the Moroccan medical context could be limited due to the lack of a dialectal Arabic version for Morocco. This study’s objective was to translate and validate the RCSQ into Arabic for Moroccan speakers. Patients and methods: For this investigation, a cross-sectional design was adopted. The RCSQ was translated and validated into Arabic for Morocco in accordance with the recommendations. For every scale, psychometric properties were computed. The Cronbach’s α coefficient was utilized to evaluate the internal consistency of multi-item measures. Results: The study involved 224 patients, whose mean age was 47 ± 18.3 years. The RCSQ’s internal consistency, or Cronbach’s alpha, was computed, and all dimensions showed good reliability over the 0.92 (0.894–0.983) level. The items demonstrated good reliability and validity, with correlation values larger than 0.4, according to the data. Conclusion: The RCSQ translated into Arabic for Morocco appears to have good psychometric qualities, making it useful for assessing the quality of sleep of patients in intensive care units within Moroccan healthcare settings.

Sleep in intensive care unit (ICU) patients is frequently disrupted, which may adversely affect their overall health and recovery. Despite the implementation of various strategies to promote sleep, accurately assessing its quality remains complex. This pilot study aimed to evaluate both the quality and quantity of sleep in ICU patients using actigraphy (ACT) and the Richards–Campbell Sleep Questionnaire (RCSQ) and to compare the diagnostic performance of these two tools. We conducted a prospective observational study including 228 ICU patients. Sleep was assessed using both RCSQ and ACT. Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the discriminative ability of each tool (Area Under the Curve [AUC], sensitivity, specificity), with optimal cut-off points determined using Youden’s Index. The Mann–Whitney U test was used to compare sleep parameters between patients classified as having good or poor sleep based on ACT measurements. The mean RCSQ score was 38.16 ± 17.09, indicating poor perceived sleep quality. Sleep onset latency (based on RCSQ) was 35.71 ± 21.44 min, with a mean of 40.32 ± 20.03 awakenings. According to ACT, sleep latency was 39.23 ± 22.09 min, and total sleep duration was 198.15 ± 128.42 min (approximately 3 h and 18 min), which is significantly below recommended levels. The average number of awakenings recorded was 24.85. In terms of diagnostic performance, the RCSQ demonstrated excellent discriminative ability (AUC = 1.00 for the total score), while ACT showed more variable results: total sleep duration had a good AUC of 0.91, while sleep latency showed a lower performance with an AUC of 0.50. The RCSQ proved to be more reliable than ACT in assessing sleep quality in ICU patients, providing consistent results across multiple parameters, including sleep depth, latency, and number of awakenings. Conversely, ACT yielded less consistent findings, particularly regarding sleep latency and nighttime interruptions. Further studies are warranted to refine objective tools for evaluating sleep in critically ill patients.

Introduction: Sleep disturbances are a commonly underestimated complication of intensive care unit (ICU) stays. This study aimed to assess sleep quality among ICU patients and to analyze the environmental and clinical factors influencing sleep quality during their stay in the ICU. Methods: We conducted a six-month cross-sectional study involving patients who stayed in the ICU for ≥ 3 nights and were oriented to time and place upon discharge. Sleep quality was assessed using the Arabic version of the Freedman sleep questionnaire, examining both environmental and non-environmental factors, such as illness severity and pain. Differences across three periods were analyzed using the Wilcoxon test and Spearman correlation, while multiple regression analyses identified factors influencing sleep quality. Statistical analyses were performed using JAMOVI software (version 2.3.28). Results: 328 patients were recruited. The average age was 49.74 ± 17.89 years. 75.3% of the participants were adults. The primary admission reasons were circulatory distress 45.73%, followed by metabolic disorders 24.09%. Self-reported sleep quality score at home and the ICU strongly indicated a difference in sleep quality between the two environments (Z = -14.870, p < 0.001), with significantly lower ranks for sleep quality in the ICU. The EVA and APACHE II scores showed a statistically significant effect on sleep quality (p < 0.001 and p= 0.015), while the Charlson and Quick Sofa scores are not significant (p = 0.128 and p=0.894). Several environmental factors showed a statistically significant impact on sleep quality such noise (p = 0.008), light (p = 0.009), and nursing intervention (p=0.009) significantly influence sleep quality. Conclusion: Patients generally reported a poor perception of nighttime sleep quality. Enhancing the pain management and minimizing both environmental noise and staff-related disturbances could significantly improve their sleep quality.