Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients.

OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC. RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08). CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

Assessing the impact of real-time random safety audits through full propensity score matching on reliable data from the clinical information system.

BACKGROUND: Evidence-based care processes are not always applied at the bedside in critically ill patients. Numerous studies have assessed the impact of checklists and related strategies on the process of care and patient outcomes. We aimed to evaluate the effects of real-time random safety audits on process-of-care and outcome variables in critical care patients. METHODS: This prospective study used data from the clinical information system to evaluate the impact of real-time random safety audits targeting 32 safety measures in two intensive care units during a 9-month period. We compared endpoints between patients attended with safety audits and those not attended with safety audits. The primary endpoint was mortality, measured by Cox hazard regression after full propensity-score matching. Secondary endpoints were the impact on adherence to process-of-care measures and on quality indicators. RESULTS: We included 871 patients; 228 of these were attended in ≥ 1 real-time random safety audits. Safety audits were carried out on 390 patient-days; most improvements in the process of care were observed in safety measures related to mechanical ventilation, renal function and therapies, nutrition, and clinical information system. Although the group of patients attended in safety audits had more severe disease at ICU admission [APACHE II score 21 (16-27) vs. 20 (15-25), p = 0.023]; included a higher proportion of surgical patients [37.3 % vs. 26.4 %, p = 0.003] and a higher proportion of mechanically ventilated patients [72.8 % vs. 40.3 %, p < 0.001]; averaged more days on mechanical ventilation, central venous catheter, and urinary catheter; and had a longer ICU stay [12.5 (5.5-23.3) vs. 2.9 (1.7-5.9), p < 0.001], ICU mortality did not differ significantly between groups (19.3 % vs. 18.8 % in the group without safety rounds). After full propensity-score matching, Cox hazard regression analysis showed real-time random safety audits were associated with a lower risk of mortality throughout the ICU stay (HR 0.31; 95 %CI 0.20-0.47). CONCLUSIONS: Real-time random safety audits are associated with a reduction in the risk of ICU mortality. Exploiting data from the clinical information system is useful in assessing the impact of them on the care process, quality indicators, and mortality.

Association of obesity on the outcome of critically ill patients affected by COVID-19.

OBJECTIVE: To evaluate the impact of obesity on ICU mortality. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adults patients admitted with COVID-19 and respiratory failure. INTERVENTIONS: None. PRIMARY VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. Body mass index (BMI) impact on ICU mortality was studied as (1) a continuous variable, (2) a categorical variable obesity/non-obesity, and (3) as categories defined a priori: underweight, normal, overweight, obesity and Class III obesity. The impact of obesity on mortality was assessed by multiple logistic regression and Smooth Restricted cubic (SRC) splines for Cox hazard regression. RESULTS: 5,206 patients were included, 20 patients (0.4%) as underweight, 887(17.0%) as normal, 2390(46%) as overweight, 1672(32.1) as obese and 237(4.5%) as class III obesity. The obesity group patients (n = 1909) were younger (61 vs. 65 years, p < 0.001) and with lower severity scores APACHE II (13 [9-17] vs. 13[10-17, p < 0.01) than non-obese. Overall ICU mortality was 28.5% and not different for obese (28.9%) or non-obese (28.3%, p = 0.65). Only Class III obesity (OR = 2.19, 95%CI 1.44-3.34) was associated with ICU mortality in the multivariate and SRC analysis. CONCLUSIONS: COVID-19 patients with a BMI > 40 are at high risk of poor outcomes in the ICU. An effective vaccination schedule and prolonged social distancing should be recommended.

Effects and safety of hypertonic saline combined with airway clearance in non-hospitalized children with recurrent wheezing.

Background: The International Study of Wheezing in Infants defines recurrent wheezing as the presence of three or more medically documented episodes of wheezing within one year. To date, there is no evidence on the use of hypertonic saline (HS) combined with airway clearance techniques (ACT) for children with recurrent wheezing treated in an outpatient setting. Therefore, this is the first study to explore the use of such interventions in infants with recurrent wheezing. Objectives: To evaluate the effects and safety of a three-month protocol including HS and ACT for non-hospitalized infants with recurrent wheezing. Methods: Randomized, double-blind, controlled trial, including outpatient infants with recurrent wheezing. Children were randomized to either 3% HS or 0.9% saline groups and were treated with bronchodilator and nebulized with the respective solutions before ACT. The primary outcome was the Wang score. Secondary outcomes included the number of hospitalizations and respiratory crisis, need for rescue medication, and school absences. All variables were measured during the three previous months from inclusion and during intervention period. The study protocol was registered at ClinicalTrials.gov (NCT04331496) on March, 31, 2020. Results: Forty children were included. Regarding immediate effects, significant differences (p<0.001) were found for time, but not for group or interaction (group × time), in all outcome variables (increase in SpO2, decrease in heart and respiratory rate, wheezing episodes, retraction, and Wang score). Comparing the previous three months with the study period, there were significant differences in both groups for the severity of crisis (p<0.001) and medication steps (p=0.002). Conclusion: A three-month protocol including HS and ACT for outpatient infants with recurrent wheezing was safe and reduced morbidity. No differences were found between the use of HS and 0.9% saline.

Impact of macrolide treatment on long-term mortality in patients admitted to the ICU due to CAP: a targeted maximum likelihood estimation and survival analysis.

INTRODUCTION: Patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU) have high mortality rates during the acute infection and up to ten years thereafter. Recommendations from international CAP guidelines include macrolide-based treatment. However, there is no data on the long-term outcomes of this recommendation. Therefore, we aimed to determine the impact of macrolide-based therapy on long-term mortality in this population. METHODS: Registered patients in the MIMIC-IV database 16 years or older and admitted to the ICU due to CAP were included. Multivariate analysis, targeted maximum likelihood estimation (TMLE) to simulate a randomised controlled trial, and survival analyses were conducted to test the effect of macrolide-based treatment on mortality six-month (6 m) and twelve-month (12 m) after hospital admission. A sensitivity analysis was performed excluding patients with Pseudomonas aeruginosa or MRSA pneumonia to control for Healthcare-Associated Pneumonia (HCAP). RESULTS: 3775 patients were included, and 1154 were treated with a macrolide-based treatment. The non-macrolide-based group had worse long-term clinical outcomes, represented by 6 m [31.5 (363/1154) vs 39.5 (1035/2621), p < 0.001] and 12 m mortality [39.0 (450/1154) vs 45.7 (1198/2621), p < 0.001]. The main risk factors associated with long-term mortality were Charlson comorbidity index, SAPS II, septic shock, and respiratory failure. Macrolide-based treatment reduced the risk of dying at 6 m [HR (95% CI) 0.69 (0.60, 0.78), p < 0.001] and 12 m [0.72 (0.64, 0.81), p < 0.001]. After TMLE, the protective effect continued with an additive effect estimate of - 0.069. CONCLUSION: Macrolide-based treatment reduced the hazard risk of long-term mortality by almost one-third. This effect remains after simulating an RCT with TMLE and the sensitivity analysis for the HCAP classification.

Negative predictive value of procalcitonin to rule out bacterial respiratory co-infection in critical covid-19 patients.

BACKGROUND: Procalcitonin (PCT) and C-Reactive Protein (CRP) are useful biomarkers to differentiate bacterial from viral or fungal infections, although the association between them and co-infection or mortality in COVID-19 remains unclear. METHODS: The study represents a retrospective cohort study of patients admitted for COVID-19 pneumonia to 84 ICUs from ten countries between (March 2020-January 2021). Primary outcome was to determine whether PCT or CRP at admission could predict community-acquired bacterial respiratory co-infection (BC) and its added clinical value by determining the best discriminating cut-off values. Secondary outcome was to investigate its association with mortality. To evaluate the main outcome, a binary logistic regression was performed. The area under the curve evaluated diagnostic performance for BC prediction. RESULTS: 4635 patients were included, 7.6% fulfilled BC diagnosis. PCT (0.25[IQR 0.1-0.7] versus 0.20[IQR 0.1-0.5]ng/mL, p<0.001) and CRP (14.8[IQR 8.2-23.8] versus 13.3 [7-21.7]mg/dL, p=0.01) were higher in BC group. Neither PCT nor CRP were independently associated with BC and both had a poor ability to predict BC (AUC for PCT 0.56, for CRP 0.54). Baseline values of PCT<0.3ng/mL, could be helpful to rule out BC (negative predictive value 91.1%) and PCT≥0.50ng/mL was associated with ICU mortality (OR 1.5,p<0.001). CONCLUSIONS: These biomarkers at ICU admission led to a poor ability to predict BC among patients with COVID-19 pneumonia. Baseline values of PCT<0.3ng/mL may be useful to rule out BC, providing clinicians a valuable tool to guide antibiotic stewardship and allowing the unjustified overuse of antibiotics observed during the pandemic, additionally PCT≥0.50ng/mL might predict worsening outcomes.

Corticosteroid treatment and mortality in mechanically ventilated COVID-19-associated acute respiratory distress syndrome (ARDS) patients: a multicentre cohort study.

BACKGROUND: Some unanswered questions persist regarding the effectiveness of corticosteroids for severe coronavirus disease 2019 (COVID-19) patients. We aimed to assess the clinical effect of corticosteroids on intensive care unit (ICU) mortality among mechanically ventilated COVID-19-associated acute respiratory distress syndrome (ARDS) patients. METHODS: This was a retrospective study of prospectively collected data conducted in 70 ICUs (68 Spanish, one Andorran, one Irish), including mechanically ventilated COVID-19-associated ARDS patients admitted between February 6 and September 20, 2020. Individuals who received corticosteroids for refractory shock were excluded. Patients exposed to corticosteroids at admission were matched with patients without corticosteroids through propensity score matching. Primary outcome was all-cause ICU mortality. Secondary outcomes were to compare in-hospital mortality, ventilator-free days at 28 days, respiratory superinfection and length of stay between patients with corticosteroids and those without corticosteroids. We performed survival analysis accounting for competing risks and subgroup sensitivity analysis. RESULTS: We included 1835 mechanically ventilated COVID-19-associated ARDS, of whom 1117 (60.9%) received corticosteroids. After propensity score matching, ICU mortality did not differ between patients treated with corticosteroids and untreated patients (33.8% vs. 30.9%; p = 0.28). In survival analysis, corticosteroid treatment at ICU admission was associated with short-term survival benefit (HR 0.53; 95% CI 0.39-0.72), although beyond the 17th day of admission, this effect switched and there was an increased ICU mortality (long-term HR 1.68; 95% CI 1.16-2.45). The sensitivity analysis reinforced the results. Subgroups of age < 60 years, severe ARDS and corticosteroids plus tocilizumab could have greatest benefit from corticosteroids as short-term decreased ICU mortality without long-term negative effects were observed. Larger length of stay was observed with corticosteroids among non-survivors both in the ICU and in hospital. There were no significant differences for the remaining secondary outcomes. CONCLUSIONS: Our results suggest that corticosteroid treatment for mechanically ventilated COVID-19-associated ARDS had a biphasic time-dependent effect on ICU mortality. Specific subgroups showed clear effect on improving survival with corticosteroid use. Therefore, further research is required to identify treatment-responsive subgroups among the mechanically ventilated COVID-19-associated ARDS patients.

Mortality comparison between the first and second/third waves among 3,795 critical COVID-19 patients with pneumonia admitted to the ICU: A multicentre retrospective cohort study.

BACKGROUND: It is unclear whether the changes in critical care throughout the pandemic have improved the outcomes in coronavirus disease 2019 (COVID-19) patients admitted to the intensive care units (ICUs). METHODS: We conducted a retrospective cohort study in adults with COVID-19 pneumonia admitted to 73 ICUs from Spain, Andorra and Ireland between February 2020 and March 2021. The first wave corresponded with the period from February 2020 to June 2020, whereas the second/third waves occurred from July 2020 to March 2021. The primary outcome was ICU mortality between study periods. Mortality predictors and differences in mortality between COVID-19 waves were identified using logistic regression. FINDINGS: As of March 2021, the participating ICUs had included 3795 COVID-19 pneumonia patients, 2479 (65·3%) and 1316 (34·7%) belonging to the first and second/third waves, respectively. Illness severity scores predicting mortality were lower in the second/third waves compared with the first wave according with the Acute Physiology and Chronic Health Evaluation system (median APACHE II score 12 [IQR 9-16] vs 14 [IQR 10-19]) and the organ failure assessment score (median SOFA 4 [3-6] vs 5 [3-7], p<0·001). The need of invasive mechanical ventilation was high (76·1%) during the whole study period. However, a significant increase in the use of high flow nasal cannula (48·7% vs 18·2%, p<0·001) was found in the second/third waves compared with the first surge. Significant changes on treatments prescribed were also observed, highlighting the remarkable increase on the use of corticosteroids to up to 95.9% in the second/third waves. A significant reduction on the use of tocilizumab was found during the study (first wave 28·9% vs second/third waves 6·2%, p<0·001), and a negligible administration of lopinavir/ritonavir, hydroxychloroquine, and interferon during the second/third waves compared with the first wave. Overall ICU mortality was 30·7% (n = 1166), without significant differences between study periods (first wave 31·7% vs second/third waves 28·8%, p = 0·06). No significant differences were found in ICU mortality between waves according to age subsets except for the subgroup of 61-75 years of age, in whom a reduced unadjusted ICU mortality was observed in the second/third waves (first 38·7% vs second/third 34·0%, p = 0·048). Non-survivors were older, with higher severity of the disease, had more comorbidities, and developed more complications. After adjusting for confounding factors through a multivariable analysis, no significant association was found between the COVID-19 waves and mortality (OR 0·81, 95% CI 0·64-1·03; p = 0·09). Ventilator-associated pneumonia rate increased significantly during the second/third waves and it was independently associated with ICU mortality (OR 1·48, 95% CI 1·19-1·85, p<0·001). Nevertheless, a significant reduction both in the ICU and hospital length of stay in survivors was observed during the second/third waves. INTERPRETATION: Despite substantial changes on supportive care and management, we did not find significant improvement on case-fatality rates among critical COVID-19 pneumonia patients. FUNDING: Ricardo Barri Casanovas Foundation (RBCF2020) and SEMICYUC.

Varying association of laboratory values with reference ranges and outcomes in critically ill patients: an analysis of data from five databases in four countries across Asia, Europe and North America.

BACKGROUND: Despite wide usage across all areas of medicine, it is uncertain how useful standard reference ranges of laboratory values are for critically ill patients. OBJECTIVES: The aim of this study is to assess the distributions of standard laboratory measurements in more than 330 selected intensive care units (ICUs) across the USA, Amsterdam, Beijing and Tarragona; compare differences and similarities across different geographical locations and evaluate how they may be associated with differences in length of stay (LOS) and mortality in the ICU. METHODS: A multi-centre, retrospective, cross-sectional study of data from five databases for adult patients first admitted to an ICU between 2001 and 2019 was conducted. The included databases contained patient-level data regarding demographics, interventions, clinical outcomes and laboratory results. Kernel density estimation functions were applied to the distributions of laboratory tests, and the overlapping coefficient and Cohen standardised mean difference were used to quantify differences in these distributions. RESULTS: The 259 382 patients studied across five databases in four countries showed a high degree of heterogeneity with regard to demographics, case mix, interventions and outcomes. A high level of divergence in the studied laboratory results (creatinine, haemoglobin, lactate, sodium) from the locally used reference ranges was observed, even when stratified by outcome. CONCLUSION: Standardised reference ranges have limited relevance to ICU patients across a range of geographies. The development of context-specific reference ranges, especially as it relates to clinical outcomes like LOS and mortality, may be more useful to clinicians.

Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts.

While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87-99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

Methods and measures to quantify ICU patient heterogeneity.

Patients in intensive care units are heterogeneous and the daily prediction of their days to discharge (DTD) a complex task that practitioners and computers are not always able to solve satisfactorily. In order to make more precise DTD predictors, it is necessary to have tools for the analysis of the heterogeneity of the patients. Unfortunately, the number of publications in this field is almost non-existent. In order to alleviate this lack of tools, we propose four methods and their corresponding measures to quantify the heterogeneity of intensive patients in the process of determining the DTD. These new methods and measures have been tested with patients admitted over four years to a tertiary hospital in Spain. The results deepen the understanding of the intensive patient and can serve as a basis for the construction of better DTD predictors.

Prognostic Value of Procalcitonin and C-Reactive Protein in 1608 Critically Ill Patients with Severe Influenza Pneumonia.

Background: Procalcitonin (PCT) and C-Reactive protein (CRP) are well-established sepsis biomarkers. The association of baseline PCT levels and mortality in pneumonia remains unclear, and we still do not know whether biomarkers levels could be related to the causative microorganism (GPC, GNB). The objective of this study is to address these issues. Methods: a retrospective observational cohort study was conducted in 184 Spanish ICUs (2009-2018). Results: 1608 patients with severe influenza pneumonia with PCT and CRP available levels on admission were included, 1186 with primary viral pneumonia (PVP) and 422 with bacterial Co-infection (BC). Those with BC presented higher PCT levels (4.25 [0.6-19.5] versus 0.6 [0.2-2.3]ng/mL) and CRP (36.7 [20.23-118] versus 28.05 [13.3-109]mg/dL) as compared to PVP (p < 0.001). Deceased patients had higher PCT (ng/mL) when compared with survivors, in PVP (0.82 [0.3-2.8]) versus 0.53 [0.19-2.1], p = 0.001) and BC (6.9 [0.93-28.5] versus 3.8 [0.5-17.37], p = 0.039). However, no significant association with mortality was observed in the multivariate analysis. The PCT levels (ng/mL) were significantly higher in polymicrobial infection (8.4) and GPC (6.9) when compared with GNB (1.2) and Aspergillus (1.7). The AUC-ROC of PCT for GPC was 0.67 and 0.32 for GNB. The AUROC of CRP was 0.56 for GPC and 0.39 for GNB. Conclusions: a single PCT/CRP value at ICU admission was not associated with mortality in severe influenza pneumonia. None of the biomarkers have enough discriminatory power to be used for predicting the causative microorganism of the co-infection.

Early oseltamivir treatment improves survival in critically ill patients with influenza pneumonia.

BACKGROUND: The relationship between early oseltamivir treatment (within 48 h of symptom onset) and mortality in patients admitted to intensive care units (ICUs) with severe influenza is disputed. This study aimed to investigate the association between early oseltamivir treatment and ICU mortality in critically ill patients with influenza pneumonia. METHODS: This was an observational study of patients with influenza pneumonia admitted to 184 ICUs in Spain during 2009-2018. The primary outcome was to evaluate the association between early oseltamivir treatment and ICU mortality compared with later treatment. Secondary outcomes were to compare the duration of mechanical ventilation and ICU length of stay between the early and later oseltamivir treatment groups. To reduce biases related to observational studies, propensity score matching and a competing risk analysis were performed. RESULTS: During the study period, 2124 patients met the inclusion criteria. All patients had influenza pneumonia and received oseltamivir before ICU admission. Of these, 529 (24.9%) received early oseltamivir treatment. In the multivariate analysis, early treatment was associated with reduced ICU mortality (OR 0.69, 95% CI 0.51-0.95). After propensity score matching, early oseltamivir treatment was associated with improved survival rates in the Cox regression (hazard ratio 0.77, 95% CI 0.61-0.99) and competing risk (subdistribution hazard ratio 0.67, 95% CI 0.53-0.85) analyses. The ICU length of stay and duration of mechanical ventilation were shorter in patients receiving early treatment. CONCLUSIONS: Early oseltamivir treatment is associated with improved survival rates in critically ill patients with influenza pneumonia, and may decrease ICU length of stay and mechanical ventilation duration.

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.

BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.

Soporte a la organización de las Unidades de Cuidados Intensivos durante la pandemia, a través de mapas creados a partir de los Sistemas de Información Clínica / Support to the organization of the Intensive Care Units during the pandemic through maps created from the Clinical Information Systems

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A Machine Learning decision-making tool for extubation in Intensive Care Unit patients.

BACKGROUND AND OBJECTIVE: To increase the success rate of invasive mechanical ventilation weaning in critically ill patients using Machine Learning models capable of accurately predicting the outcome of programmed extubations. METHODS: The study population was adult patients admitted to the Intensive Care Unit. Target events were programmed extubations, both successful and failed. The working dataset is assembled by combining heterogeneous data including time series from Clinical Information Systems, patient demographics, medical records and respiratory event logs. Three classification learners have been compared: Logistic Discriminant Analysis, Gradient Boosting Method and Support Vector Machines. Standard methodologies have been used for preprocessing, hyperparameter tuning and resampling. RESULTS: The Support Vector Machine classifier is found to correctly predict the outcome of an extubation with a 94.6% accuracy. Contrary to current decision-making criteria for extubation based on Spontaneous Breathing Trials, the classifier predictors only require monitor data, medical entry records and patient demographics. CONCLUSIONS: Machine Learning-based tools have been found to accurately predict the extubation outcome in critical patients with invasive mechanical ventilation. The use of this important predictive capability to assess the extubation decision could potentially reduce the rate of extubation failure, currently at 9%. With about 40% of critically ill patients eventually receiving invasive mechanical ventilation during their stay and given the serious potential complications associated to reintubation, the excellent predictive ability of the model presented here suggests that Machine Learning techniques could significantly improve the clinical outcomes of critical patients.

Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit.

BACKGROUND: Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS). OBJECTIVE: To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data. METHODS: We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period. RESULTS: We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society. CONCLUSIONS: This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.

Infección grave por Coronavirus SARS-CoV-2: Experiencia en un hospital de tercer nivel con pacientes afectados por COVID-19 durante la pandemia 2020 / Severe infection due to the SARS-CoV-2 CORONAVIRUS: Experience of a tertiary hospital with COVID-19 patients during the 2020 pandemic

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