Machine learning-based dynamic mortality prediction after traumatic brain injury

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries.Peer reviewe

Machine learning-based dynamic mortality prediction after traumatic brain injury

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries

A descriptive study of the surge response and outcomes of ICU patients with COVID-19 during first wave in Nordic countries

Abstract Background We sought to provide a description of surge response strategies and characteristics, clinical management and outcomes of patients with severe COVID-19 in the intensive care unit (ICU) during the first wave of the pandemic in Denmark, Finland, Iceland, Norway and Sweden. Methods Representatives from the national ICU registries for each of the five countries provided clinical data and a description of the strategies to allocate ICU resources and increase the ICU capacity during the pandemic. All adult patients admitted to the ICU for COVID-19 disease during the first wave of COVID-19 were included. The clinical characteristics, ICU management and outcomes of individual countries were described with descriptive statistics. Results Most countries more than doubled their ICU capacity during the pandemic. For patients positive for SARS-CoV-2, the ratio of requiring ICU admission for COVID-19 varied substantially (1.6-6.7%). Apart from age (proportion of patients aged 65 years or over between 29-62%), baseline characteristics, chronic comorbidity burden and acute presentations of COVID-19 disease were similar among the five countries. While utilization of invasive mechanical ventilation was high (59-85%) in all countries, the proportion of patients receiving renal replacement therapy (7-26%) and various experimental therapies for COVID-19 disease varied substantially (e.g. use of hydroxychloroquine 0-85%). Crude ICU mortality ranged from 11% to 33%. Conclusion There was substantial variability in the critical care response in Nordic ICUs to the first wave of COVID-19 pandemic, including usage of experimental medications. While ICU mortality was low in all countries, the observed variability warrants further attention.Peer reviewe

External validation of the NeuroImaging Radiological Interpretation System and Helsinki computed tomography score for mortality prediction in patients with traumatic brain injury treated in the intensive care unit:a Finnish intensive care consortium study

Abstract Background: Admission computed tomography (CT) scoring systems can be used to objectively quantify the severity of traumatic brain injury (TBI) and aid in outcome prediction. We aimed to externally validate the NeuroImaging Radiological Interpretation System (NIRIS) and the Helsinki CT score. In addition, we compared the prognostic performance of the NIRIS and the Helsinki CT score to the Marshall CT classification and to a clinical model. Methods: We conducted a retrospective multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted in four university hospital ICUs during 2003–2013. We analyzed the CT scans using the NIRIS and the Helsinki CT score and compared the results to 6-month mortality as the primary outcome. In addition, we created a clinical model (age, Glasgow Coma Scale score, Simplified Acute Physiology Score II, presence of severe comorbidity) and combined clinical and CT models to see the added predictive impact of radiological data to conventional clinical information. We measured model performance using area under curve (AUC), Nagelkerke’s R2 statistics, and the integrated discrimination improvement (IDI). Results: A total of 3031 patients were included in the analysis. The 6-month mortality was 710 patients (23.4%). Of the CT models, the Helsinki CT displayed best discrimination (AUC 0.73 vs. 0.70 for NIRIS) and explanatory variation (Nagelkerke’s R² 0.20 vs. 0.15). The clinical model displayed an AUC of 0.86 (95% CI 0.84–0.87). All CT models increased the AUC of the clinical model by + 0.01 to 0.87 (95% CI 0.85–0.88) and the IDI by 0.01–0.03. Conclusion: In patients with TBI treated in the ICU, the Helsinki CT score outperformed the NIRIS for 6-month mortality prediction. In isolation, CT models offered only moderate accuracy for outcome prediction and clinical variables outweighing the CT-based predictors in terms of predictive performance

Psychotropic medication use among patients with a traumatic brain injury treated in the intensive care unit:a multi-centre observational study

Abstract Background: Psychiatric sequelae after traumatic brain injury (TBI) are common and may impede recovery. We aimed to assess the occurrence and risk factors of post-injury psychotropic medication use in intensive care unit (ICU)-treated patients with TBI and its association with late mortality. Methods: We conducted a retrospective multi-centre observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted in four university hospital ICUs during 2003–2013 that were alive at 1 year after injury. Patients were followed-up until end of 2016. We obtained data regarding psychotropic medication use through the national drug reimbursement database. We used multivariable logistic regression models to assess the association between TBI severity, treatment-related variables and the odds of psychotropic medication use and its association with late all-cause mortality (more than 1 year after TBI). Results: Of 3061 patients, 2305 (75%) were alive at 1 year. Of these, 400 (17%) became new psychotropic medication users. The most common medication types were antidepressants (61%), antipsychotics (35%) and anxiolytics (26%). A higher Glasgow Coma Scale (GCS) score was associated with lower odds (OR 0.93, 95% CI 0.90–0.96) and a diffuse injury with midline shift was associated with higher odds (OR 3.4, 95% CI 1.3–9.0) of new psychotropic medication use. After adjusting for injury severity, new psychotropic medication use was associated with increased odds of late mortality (OR 1.19, 95% CI 1.19–2.17, median follow-up time 6.4 years). Conclusions: Psychotropic medication use is common in TBI survivors. Higher TBI severity is associated with increased odds of psychotropic medication use. New use of psychotropic medications after TBI was associated with increased odds of late mortality. Our results highlight the need for early identification of potential psychiatric sequelae and psychiatric evaluation in TBI survivors

Association of endothelial and glycocalyx injury biomarkers with fluid administration, development of acute kidney injury, and 90-day mortality:data from the FINNAKI observational study

Abstract Background: Injury to endothelium and glycocalyx predisposes to vascular leak, which may subsequently lead to increased fluid requirements and worse outcomes. In this post hoc study of the prospective multicenter observational Finnish Acute Kidney Injury (FINNAKI) cohort study conducted in 17 Finnish intensive care units, we studied the association of Syndecan-1 (SDC-1), Angiopoetin-2 (Ang-2), soluble thrombomodulin (sTM), vascular adhesion protein-1 (VAP-1) and interleukin-6 (IL-6) with fluid administration and balance among septic critical care patients and their association with development of acute kidney injury (AKI) and 90-day mortality. Results: SDC-1, Ang-2, sTM, VAP-1 and IL-6 levels were measured at ICU admission from 619 patients with sepsis. VAP-1 decreased (p < 0.001) and IL-6 increased (p < 0.001) with increasing amounts of administered fluid, but other biomarkers did not show differences according to fluid administration. In linear regression models adjusted for IL-6, only VAP-1 was significantly associated with fluid administration on day 1 (p < 0.001) and the cumulative fluid balance on day 5/ICU discharge (p = 0.001). Of 415 patients admitted without AKI, altogether 112 patients (27.0%) developed AKI > 12 h from ICU admission (AKI>12 h). They had higher sTM levels than patients without AKI, and after multivariable adjustment log, sTM level was associated with AKI>12 h with OR (95% CI) of 12.71 (2.96–54.67), p = 0.001). Ninety-day non-survivors (n = 180; 29.1%) had higher SDC-1 and sTM levels compared to survivors. After adjustment for known confounders, log SDC-1 (OR [95% CI] 2.13 [1.31–3.49], p = 0.002), log sTM (OR [95% CI] 7.35 [2.29–23.57], p < 0.001), and log Ang-2 (OR [95% CI] 2.47 [1.44–4.14], p = 0.001) associated with an increased risk for 90-day mortality. Finally, patients who had high levels of all three markers, namely, SDC-1, Ang-2 and sTM, had an adjusted OR of 5.61 (95% CI 2.67–11.79; p < 0.001) for 90-day mortality. Conclusions: VAP-1 and IL-6 associated with fluid administration on the first ICU day. After adjusting for confounders, sTM was associated with development of AKI after 12 h from ICU admission. SDC-1, Ang-2 and sTM were independently associated with an increased risk for 90-day mortality

Posttraumatic epilepsy in intensive care unit–treated pediatric traumatic brain injury patients

Abstract Objective: Posttraumatic epilepsy (PTE) is a well‐described complication of traumatic brain injury (TBI). The majority of the available data regarding PTE stem from the adult population. Our aim was to identify the clinical and radiological risk factors associated with PTE in a pediatric TBI population treated in an intensive care unit (ICU). Methods: We used the Finnish Intensive Care Consortium database to identify pediatric (<18 years) TBI patients treated in four academic university hospital ICUs in Finland between 2003 and 2013. Our primary outcome was the development of PTE, defined as the need for oral antiepileptic medication in patients alive at 6 months. We assessed the risk factors associated with PTE using multivariable logistic regression modeling. Results: Of the 290 patients included in the study, 59 (20%) developed PTE. Median age was 15 years (interquartile range [IQR] 13‐17), and 80% had an admission Glasgow Coma Scale (GCS) score ≤12. Major risk factors for developing PTE were age (adjusted odds ratio [OR] 1.08, 95% confidence interval [CI] 1.00‐1.16), obliterated suprasellar cisterns (OR 6.53, 95% CI 1.95‐21.81), and an admission GCS score of 9‐12 in comparison to a GCS score of 13‐15 (OR 2.88, 95% CI 1.24‐6.69). Significance: We showed that PTE is a common long‐term complication after ICU‐treated pediatric TBI. Higher age, moderate injury severity, obliterated suprasellar cisterns, seizures during ICU stay, and surgical treatment are associated with an increased risk of PTE. Further studies are needed to identify strategies to decrease the risk of PTE