Delirium is one of the most frequently reported neuropsychiatric complications in the perioperative period, especially in the population of elderly patients who often suffer from numerous comorbidities undergoing extensive or urgent surgery. It can affect up to 80% of patients who require hospitalization in an intensive care setting postoperatively. Delirium increases mortality, morbidity, length of hospital stay, and cost of treatment. An episode of delirium in the acute phase may lower the general quality of life and increases the risk of cognitive decline long-term. Since pharmacological treatment of delirium is not highly effective, focus of research has shifted towards developing preventive strategies. We aimed to perform a review of the topic based on the most recent literature. We conclude that, based on the available data, it seems impossible to make strong recommendations for using antipsychotic drugs in prophylaxis. Further research should answer the question what, if any, benefit patients receive from the pharmacological prevention of delirium, and which agents should be used.

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Background: Advanced hemodynamic monitoring supports making therapeutic decisions in critically ill patients. New technologies, including mobile health, have been introduced into the hemodynamic monitoring armamentarium. However, each monitoring method has potential limitations—content, technical and organizational. The aim of this study was to assess the comparability between measurements obtained with two arterial pressure cardiac output methods: Capstesia™ smartphone hemodynamic software (CS) and LiDCO Rapid™ uncalibrated hemodynamic monitor (LR). Methods: The initial analysis included 16 patients in the period 06–09 2020 without limitations that could make the results obtained unreliable. Eighty pairs of cardiac output measurements were obtained. The comparability of cardiac output results obtained with both methods was assessed using the Spearman’s rank correlation coefficient (R), the intra-class correlation (CCC) and the Bland–Altman curves analysis (B-A). Results: The median (IQR) cardiac output measured with CS and LR were 4.6 (3.9–5.7) and 5.5 (4.6–7.4) L min−1, respectively. In the B-A analysis, CS cardiac output values were on average 1.2 (95% CI −2.1–4.4) L min-1 lower than LR values. The correlation between cardiac output with CS and LR was moderate (r = 0.5; p = 0.04). After adjusting for the presence of the dicrotic notch on the pulse waveform, in the group of eight patients with a visible dicrotic notch, the CS and LR results differed by only 0.1 (95% CI −0.8–1.1) L min−1, the correlation between CS and LR was close to complete (r = 0.96; p <0.001), and the percentage error was 40%, with a CCC-CS of 0.98 (95% CI 0.95–0.99). Conclusions: The CapstesiaTM smartphone software can provide an alternative method of cardiac output assessment in patients meeting arterial pressure cardiac output evaluation criteria with a clearly discernible dicrotic notch on the arterial pulse pressure waveform. It is necessary to confirm the obtained observations on a larger group of patients; however, it may potentially make objective hemodynamic measurements ubiquitous in patients with invasive arterial pressure monitoring with a clearly discernible dicrotic notch.

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Introduction. Platelets (PLT) are key mediators in thrombotic and inflammatory processes. Their activity increases with size, so the mean platelet volume (MPV) can be a potential predictor of perioperative complications. The aim of the study was to assess the suitability of platelet parameters in predicting the risk of hospital death in neurosurgery. Methods. Retrospective observation covered 452 patients undergoing surgery in the period March 2018–August 2018. High-risk patients accounted for 44% (i.e., ASA-PS class III+) and 9% (i.e., ≥1 Shoemaker criterion), respectively, and 14% of procedures were performed in the urgent mode. The preoperative platelet parameters that were assessed and analysed were: total platelet count (PLT), mean platelet volume (MPV), plateletcrit (PCT) and platelet distribution width (PDW). The end point of the study was a hospital death. Results. Before discharge from the hospital, 13 patients died. The medians (IQR) PLT, MPV PDW and PCT were, respectively: 230 × 106 L−1 (182–279); 9.2 fL (8.3–10.1); 14% (12.5–16.3); and 21% (17–26). PLT, PCT and PDW were not useful in the risk assessment. MPV was lower in patients who died (9.3 vs. 8.3 fL, p < 0.01) and predicted death occurred in 76% (AUC = 0.76, 95%CI 0.72–0.80, p < 0.01). Further, after adjustment for confounders, MPV remained a significant predictor of in-hospital death (logOR[MPV] = 0.31, AUC = 0.94, 95%CI 0.92–0.96, p = 0.02). Conclusion. The reduction in the average volume of platelets is associated with a worse prognosis in neurosurgical patients.