Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension.

Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000-2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020-2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80-0.80), 73.8% (95% CI, 72.0-75.6%), 73.5% (95% CI 72.5-74.5%), and 73.0% (95% CI, 72.0-74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07-1.32), and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all).

Development and Validation of a Policy Tree Approach for Optimizing Intravenous Fluids in Critically Ill Patients with Sepsis and Acute Kidney Injury.

Purpose: Intravenous fluids are mainstay of management of acute kidney injury (AKI) after sepsis but can cause fluid overload. Recent literature shows that restrictive fluid strategy may be beneficial in some patients with AKI, however, identifying these patients is challenging. We aimed to develop and validate a machine learning algorithm to identify patients who would benefit from a restrictive fluid strategy. Methods: We included patients with sepsis who developed AKI within 48 hours of ICU admission and defined restrictive fluid strategy as receiving <500mL fluids within 24 hours after AKI. Our primary outcome was early AKI reversal within 48 hours of AKI onset, and secondary outcomes included sustained AKI reversal and major adverse kidney events (MAKE) at discharge. We used a causal forest, a machine learning algorithm to estimate individual treatment effects and policy tree algorithm to identify patients who would benefit by restrictive fluid strategy. We developed the algorithm in MIMIC-IV and validated it in eICU database. Results: Among 2,091 patients in the external validation cohort, policy tree recommended restrictive fluids for 88.2%. Among these, patients who received restrictive fluids demonstrated significantly higher rate of early AKI reversal (48.2% vs 39.6%, p<0.001), sustained AKI reversal (36.7% vs 27.4%, p<0.001) and lower rates of MAKE by discharge (29.3% vs 35.1%, p=0.019). These results were consistent in adjusted analysis. Conclusion: Policy tree based on causal machine learning can identify septic patients with AKI who benefit from a restrictive fluid strategy. This approach needs to be validated in prospective trials.

Risk Factors and Outcomes Associated With the Development of Persistent Acute Kidney Injury in Non-Renal Solid Organ Transplant Recipients: Systematic Review and Meta-Analysis.

Persistent acute kidney injury (pAKI), compared with acute kidney injury (AKI) that resolves in <72 h, is associated with worse prognosis in critically ill patients. Definitions and prognosis of pAKI are not well characterized in solid organ transplant patients. Our aims were to investigate (a) definitions and incidence of pAKI; (b) association with clinical outcomes; and (c) risk factors for pAKI among heart, lung, and liver transplant recipients. We systematically reviewed the literature including PubMed, Embase, Web of Science, and Cochrane from inception to 8/1/2023 for human prospective and retrospective studies reporting on the development of pAKI in heart, lung, or liver transplant recipients. We assessed heterogeneity using Cochran's Q and I2. We identified 25 studies including 6330 patients. AKI (8%-71.6%) and pAKI (2.7%-55.1%) varied widely. Definitions of pAKI included 48-72 h (six studies), 7 days (three studies), 14 days (four studies), or more (12 studies). Risk factors included age, body mass index (BMI), diabetes, preoperative chronic kidney disease (CKD), intraoperative vasopressor use, and intraoperative circulatory support. pAKI was associated with new onset of CKD (odds ratio [OR] 1.41-11.2), graft dysfunction (OR 1.81-8.51), and long-term mortality (OR 3.01-13.96), although significant heterogeneity limited certainty of CKD and graft dysfunction outcome analyses. pAKI is common and is associated with worse mortality among liver and lung transplant recipients. Standardization of the nomenclature of AKI will be important in future studies (PROSPERO CRD42022371952).

Role of artificial intelligence in critical care nutrition support and research.

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Toward Equitable Kidney Function Estimation in Critical Care Practice: Guidance From the Society of Critical Care Medicine's Diversity, Equity, and Inclusion in Renal Clinical Practice Task Force.

OBJECTIVES: Accurate glomerular filtration rate (GFR) assessment is essential in critically ill patients. GFR is often estimated using creatinine-based equations, which require surrogates for muscle mass such as age and sex. Race has also been included in GFR equations, based on the assumption that Black individuals have genetically determined higher muscle mass. However, race-based GFR estimation has been questioned with the recognition that race is a poor surrogate for genetic ancestry, and racial health disparities are driven largely by socioeconomic factors. The American Society of Nephrology and the National Kidney Foundation (ASN/NKF) recommend widespread adoption of new "race-free" creatinine equations, and increased use of cystatin C as a race-agnostic GFR biomarker. DATA SOURCES: Literature review and expert consensus. STUDY SELECTION: English language publications evaluating GFR assessment and racial disparities. DATA EXTRACTION: We provide an overview of the ASN/NKF recommendations. We then apply an Implementation science methodology to identify facilitators and barriers to implementation of the ASN/NKF recommendations into critical care settings and identify evidence-based implementation strategies. Last, we highlight research priorities for advancing GFR estimation in critically ill patients. DATA SYNTHESIS: Implementation of the new creatinine-based GFR equation is facilitated by low cost and relative ease of incorporation into electronic health records. The key barrier to implementation is a lack of direct evidence in critically ill patients. Additional barriers to implementing cystatin C-based GFR estimation include higher cost and lack of test availability in most laboratories. Further, cystatin C concentrations are influenced by inflammation, which complicates interpretation. CONCLUSIONS: The lack of direct evidence in critically ill patients is a key barrier to broad implementation of newly developed "race-free" GFR equations. Additional research evaluating GFR equations in critically ill patients and novel approaches to dynamic kidney function estimation is required to advance equitable GFR assessment in this vulnerable population.

Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.

Importance: Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective: Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design: Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting: MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants: Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures: Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures: Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results: The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance: aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.

Impact of Using Blood Warmer During Continuous Kidney Replacement Therapy in Patients With Acute Kidney Injury.

PURPOSE: We investigated the impact of blood warmer use on hypotensive episodes in patients with acute kidney injury (AKI) receiving continuous kidney replacement therapy (CKRT). MATERIALS AND METHODS: We included patients with AKI undergoing CKRT between January 1, 2012, and January 1, 2021, at a tertiary academic hospital. Hypotensive episodes were defined as mean arterial pressure (MAP) <60 mm Hg or a decrease in MAP by ≥10 mm Hg, systolic blood pressure (SBP) < 90 mm Hg or a decrease in SBP by ≥20 mm Hg, or increased vasopressor requirement. These were analyzed by Poisson regression with repeated-measures analysis of variance using generalized estimation equation. RESULTS: There were 669 patients with AKI that required CKRT. Use of blood warmer on first day of CKRT was in 324 (48%) patients. Incidence rate ratio of hypotensive episodes during the first 24-h of CKRT in patients where a blood warmer was used was 1.06 (95% confidence interval [CI]: 0.98-1.13) compared to those where blood warmer was not used. This did not change in adjusted model. Overall, the within-subject effect of temperature on hypotensive episodes showed that higher temperature was associated with fewer episodes (0.94, 95% CI: 0.9-0.99 per 10 degrees increase, P = .007). CONCLUSION: Blood rewarming was not associated with hypotensive episodes during CKRT.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

BACKGROUND: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS: Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

Clinical Informatics in Critical Care Medicine.

Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.

Contemporary Coronary Artery Bypass Grafting vs Multivessel Percutaneous Coronary Intervention.

BACKGROUND: Interpretation of recent alterations to the guideline priority of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) for multivessel disease contests historical data and practice. To reevaluate contemporary outcomes, a large contemporary analysis was conducted comparing CABG with multivessel PCI among Medicare beneficiaries. METHODS: The United States Centers for Medicare and Medicaid Services database was evaluated all beneficiaries with acute coronary syndrome undergoing isolated CABG or multivessel PCI (2018-2020). Risk adjustment was performed using multilevel regression analysis, Cox proportional hazards time to event models, and inverse probability of treatment weighting propensity scores. RESULTS: A total of 104,127 beneficiaries were identified undergoing CABG (n = 51,389) or multivessel PCI (n = 52,738). Before risk adjustment, compared with PCI, CABG patients were associated with younger age (72.9 vs 75.2 years, P < .001), higher Elixhauser Comorbidity Index (5.0 vs 4.2, P < .001), more diabetes (48.5% vs 42.2%, P < .001), higher cost ($54,154 vs $33,484, P < .001), and longer length of stay (11.9 vs 5.8 days, P < .001). After inverse probability of treatment weighting propensity score adjustment, compared with PCI, CABG was associated with lower hospital mortality (odds ratio, 0.74; P < .001), fewer hospital readmissions at 3 years (odds ratio, 0.85; P < .001), fewer coronary reinterventions (hazard ratio, 0.37; P < .001), and improved 3-year survival (hazard ratio, 0.51; P < .001). CONCLUSIONS: Contemporary real-world data from Medicare patients with multivessel disease reveal that CABG outcomes were superior to PCI, providing important longitudinal data to guide patient care and policy development.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative.

BACKGROUND: AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. METHODS: Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. RESULTS: Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality. CONCLUSIONS: The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

Acceptance of Acute Kidney Injury Alert by Providers in Cardiac Surgery Intensive Care Unit.

BACKGROUND: Acute kidney injury (AKI) is a common complication after cardiac surgery and is associated with worse outcomes. Its management relies on early diagnosis, and therefore, electronic alerts have been used to alert clinicians for development of AKI. Electronic alerts are, however, associated with high rates of alert fatigue. OBJECTIVES: We designed this study to assess the acceptance of user-centered electronic AKI alert by clinicians. METHODS: We developed a user-centered electronic AKI alert that alerted clinicians of development of AKI in a persistent yet noninterruptive fashion. As the goal of the alert was to alert toward new or worsening AKI, it disappeared 48 hours after being activated. We assessed the acceptance of the alert using surveys at 6 and 12 months after the alert went live. RESULTS: At 6 months after their implementation, 38.9% providers reported that they would not have recognized AKI as early as they did without this alert. This number increased to 66.7% by 12 months of survey. Most providers also shared that they re-dosed or discontinued medications earlier, provided earlier management of volume status, avoided intravenous contrast use, and evaluated patients by using point-of-care ultrasounds more due to the alert. Overall, 83.3% respondents reported satisfaction with the electronic AKI alerts at 6 months and 94.4% at 12 months. CONCLUSION: This study showed high rates of acceptance of a user-centered electronic AKI alert over time by clinicians taking care of patients with AKI.

Ketamine in Critically Ill Patients: Use, Perceptions, and Potential Barriers.

Objective: To evaluate practitioner use of ketamine and identify potential barriers to use in acutely and critically ill patients. To compare characteristics, beliefs, and practices of ketamine frequent users and non-users. Methods: An online survey developed by members of the Society of Critical Care Medicine (SCCM) Clinical Pharmacy and Pharmacology Section was distributed to physician, pharmacist, nurse practitioner, physician assistant and nurse members of SCCM. The online survey queried SCCM members on self-reported practices regarding ketamine use and potential barriers in acute and critically ill patients. Results: Respondents, 341 analyzed, were mostly adult physicians, practicing in the United States at academic medical centers. Clinicians were comfortable or very comfortable using ketamine to facilitate intubation (80.0%), for analgesia (77.9%), procedural sedation (79.4%), continuous ICU sedation (65.8%), dressing changes (62.4%), or for asthma exacerbation and status epilepticus (58.8% and 40.4%). Clinicians were least comfortable with ketamine use for alcohol withdrawal and opioid detoxification (24.7% and 23.2%). Most respondents reported "never" or "infrequently" using ketamine preferentially for continuous IV analgesia (55.6%) or sedation (61%). Responses were mixed across dosing ranges and duration. The most common barriers to ketamine use were adverse effects (42.6%), other practitioners not routinely using the medication (41.5%), lack of evidence (33.5%), lack of familiarity (33.1%), and hospital/institutional policy guiding the indication for use (32.3%). Conclusion: Although most critical care practitioners report feeling comfortable using ketamine, there are many inconsistencies in practice regarding dose, duration, and reasons to avoid or limit ketamine use. Further educational tools may be targeted at practitioners to improve appropriate ketamine use.

COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality.

Background: Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. Methods: Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. Results: Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. Conclusions: Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill.

Background: Medication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The objective of this study was to examine the relationship between the scores of different MRC tools and the clinical outcomes. Methods: We conducted a retrospective cohort study at Roger William Medical Center, Providence, Rhode Island, which included 317 adult patients admitted to the intensive care unit (ICU) between 1 February 2020 and 30 August 2020. MRC was assessed using the MRC Index (MRCI) and MRC for the Intensive Care Unit (MRC-ICU). A multivariable logistic regression model was used to identify associations among MRC scores, clinical outcomes, and a logistic classifier to predict clinical outcomes. Results: Higher MRC scores were associated with increased mortality, a longer ICU length of stay (LOS), and the need for mechanical ventilation (MV). MRC-ICU scores at 24 h were significantly (p < 0.001) associated with increased ICU mortality, LOS, and MV, with ORs of 1.12 (95% CI: 1.06−1.19), 1.17 (1.1−1.24), and 1.21 (1.14−1.29), respectively. Mortality prediction was similar using both scoring tools (AUC: 0.88 [0.75−0.97] vs. 0.88 [0.76−0.97]. The model with 15 medication classes outperformed others in predicting the ICU LOS and the need for MV with AUCs of 0.82 (0.71−0.93) and 0.87 (0.77−0.96), respectively. Conclusion: Our results demonstrated that both MRC scores were associated with poorer clinical outcomes. The incorporation of MRC scores in real-time therapeutic decision making can aid clinicians to prescribe safer alternatives.

Do high-dose corticosteroids improve outcomes in hospitalized COVID-19 patients?

Coronavirus disease 2019 (COVID-19) is characterized by dysregulated hyperimmune response and steroids have been shown to decrease mortality. However, whether higher dosing of steroids results in better outcomes has been debated. This was a retrospective observation of COVID-19 admissions between March 1, 2020, and March 10, 2021. Adult patients (≥18 years) who received more than 10 mg daily methylprednisolone equivalent dosing (MED) within the first 14 days were included. We excluded patients who were discharged or died within 7 days of admission. We compared the standard dose of steroids (<40 mg MED) versus the high dose of steroids (>40 mg MED). Inverse probability weighted regression adjustment (IPWRA) was used to examine whether higher dose steroids resulted in improved outcomes. The outcomes studied were in-hospital mortality, rate of acute kidney injury (AKI) requiring hemodialysis, invasive mechanical ventilation (IMV), hospital-associated infections (HAI), and readmissions. Of the 1379 patients meeting study criteria, 506 received less than 40 mg of MED (median dose 30 mg MED) and 873 received more than or equal to 40 mg of MED (median dose 78 mg MED). Unadjusted in-hospital mortality was higher in patients who received high-dose corticosteroids (40.7% vs. 18.6%, p < 0.001). On IPWRA, the use of high-dose corticosteroids was associated with higher odds of death (odds ratio [OR] 2.14; 95% confidence interval [CI] 1.45-3.14, p < 0.001) but not with the development of HAI, readmissions, or requirement of IMV. High-dose corticosteroids were associated with lower rates of AKI requiring hemodialysis (OR 0.33; 95% CI 0.18-0.63). In COVID-19, corticosteroids more than or equal to 40 mg MED were associated with higher in-hospital mortality.