Hyperchloremia in critically ill patients: association with outcomes and prediction using electronic health record data

Abstract Background Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients. Methods We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays. Results Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7—a clinically-actionable rate. Conclusions Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes.

Download Full-text

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

Clinical Journal of the American Society of Nephrology ◽

10.2215/cjn.09330819 ◽

2020 ◽

Vol 15 (11) ◽

pp. 1557-1565 ◽

Cited By ~ 2

Author(s):

Kumardeep Chaudhary ◽

Akhil Vaid ◽

Áine Duffy ◽

Ishan Paranjpe ◽

Suraj Jaladanki ◽

...

Keyword(s):

Intensive Care ◽

Deep Learning ◽

Medical Information ◽

Tertiary Care ◽

Vital Signs ◽

Kidney Injury ◽

The United States ◽

Care Hospital ◽

Electronic Health Record Data ◽

Electronic Health

Background and objectivesSepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.Design, setting, participants, & measurementsWe used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.ResultsWe identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).ConclusionsUtilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

Download Full-text

Hyperchloremia is associated with aute kidney injury in critical ill patients: an analysis of the MIMIC-III database

10.21203/rs.3.rs-133533/v1 ◽

2020 ◽

Author(s):

Jie Gu ◽

Tingting Zuo ◽

Qingqing Zhu ◽

Hui Chen ◽

Yanbin Chen ◽

...

Keyword(s):

Intensive Care ◽

Medical Information ◽

Gastrointestinal Disease ◽

Kidney Injury ◽

Cerebral Disease ◽

Icu Patients ◽

Increased Risk ◽

Critical Ill Patients ◽

Mimic Iii ◽

Critical Ill

Abstract Objective: Balanced fluid with no critical increase of chloride in serum was recommended in clinic. Whether hyperchloremia could make a difference for intensive care unit (ICU) patients with a higher acute kidney injury (AKI) occurrence remains controversial.Methods: The Medical Information Mart for Intensive Care III (MIMIC-III) database was searched to identify patients hyperchloremia or non-hyperchloremia, and relationship between level of chloride and AKI incidence was analyzed using the univariate and multivariate logistic regression. Patients were divided into four disease subgroups based on the diagnosis at admission: cardiac, cerebral, gastrointestinal, respiratory. The association between maximum chloride (chloride_max) and incidence of AKI in each subgroup was evaluated using the Lowess Smoothing technique. Receiver operating characteristic curves were applied to analyze the diagnostic value of hyperchloremia (chloride_max>110mmol/L) in these four subgroup patients.Results: A total of 34,617 patients were included in our study, of which 12667 patients (36.6%) was diagnosed with hyperchloremia. The risk of incidence of AKI was increased in the hyperchloremia group. As the higher level of hyperchlorimia, the bigger adjusted odds ratio (OR) presented in terms of AKI, with the OR increasing from 1.13 (95%CI 1.06-1.21; P<0.001) to 4.09 (95%CI 3.04-5.52; P<0.001). Normal level of chloride (95-110mmol/L) was associated with the lower incidence of AKI rate compared to the hypochloremia (<95mmol/L) or the hyperchloremia (>110mmol/L) in any subgroup of cerebral, cardiac, respiratory and gastrointestinal disease. The diagnostic performance was good for cerebral disease (AUC=0.617), cardiac disease (AUC=0.636), respiratory disease (AUC=0.623) and gastrointestinal disease (AUC=0.633). The optimal cut-off value in terms of chloride_max for diagnosing AKI was 116mmol/L for the subgroup of cerebral, respiratory and gastrointestinal diseases, and 115 mmol/L for cardiac patients.Conclusion: Hyperchloremia was associated with increased risk adjusted AKI incidence among critical ill patients. For ICU patients with cerebral, gastrointestinal and respiratory admission diagnose, the predictive threshold was at 116mmoL/L, and cardiac diagnose was at 115 mmol/L.

Download Full-text

Association Between Base Excess and Mortality Among Patients in ICU With Acute Kidney Injury

Frontiers in Medicine ◽

10.3389/fmed.2021.779627 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yi Cheng ◽

You Zhang ◽

Boxiang Tu ◽

Yingyi Qin ◽

Xin Cheng ◽

...

Keyword(s):

Acute Kidney Injury ◽

Intensive Care ◽

Medical Information ◽

Proportional Hazards ◽

Base Excess ◽

Proportional Hazards Model ◽

Kidney Injury ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Icu Patients

Objective: This study aimed to explore the association between base excess (BE) and the risk of 30-day mortality among patients with acute kidney injury (AKI) in the intensive care unit (ICU).Methods: This retrospective study included patients with AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. We used a multivariate Cox proportional-hazards model to obtain the hazard ratio (HR) for the risk of 30-day mortality among patients with AKI. Furthermore, we utilized a Cox proportional-hazard model with restricted cubic splines (RCS) to explore the potential non-linear associations.Results: Among the 14,238 ICU patients with AKI, BE showed a U-shaped relationship with risk of 30-day mortality for patients with AKI, and higher or lower BE values could increase the risk. Compared with normal base excess (−3~3 mEq/L), patients in different groups (BE ≤ −9 mEq/L, −9 mEq/L < BE ≤ −3 mEq/L, 3 mEq/L < BE ≤ 9 mEq/L, and BE > 9 mEq/L) had different HRs for mortality: 1.57 (1.40, 1.76), 1.26 (1.14, 1.39), 0.97 (0.83, 1.12), 1.53 (1.17, 2.02), respectively. The RCS analyses also showed a U-shaped curve between BE and the 30-day mortality risk.Conclusion: Our results suggest that higher and lower BE in patients with AKI would increase the risk of 30-day mortality. BE measured at administration could be a critical prognostic indicator for ICU patients with AKI and provide guidance for clinicians.

Download Full-text

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Comparative Machine Learning Study (Preprint)

10.2196/preprints.23508 ◽

2020 ◽

Author(s):

Sujeong Hur ◽

Ji Young Min ◽

Junsang Yoo ◽

Kyunga Kim ◽

Chi Ryang Chung ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Patient Safety ◽

Intensive Care ◽

Predictive Value ◽

Prediction Models ◽

Support Vector ◽

Unplanned Extubation ◽

Electronic Health Record Data ◽

Icu Patients

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered as the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted an academic tertiary hospital in Seoul. The hospital had approximately 2,000 inpatient beds and 120 intensive care unit (ICU) beds. The number of patients, on daily basis, was approximately 9,000 for the out-patient. The number of annual ICU admission was approximately 10,000. We conducted a retrospective study between January 1, 2010 and December 31, 2018. A total of 6,914 extubation cases were included. We developed an unplanned extubation prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used area under the receiver operator characteristic curve (AUROC). Sensitivity, specificity, positive predictive value negative predictive value, and F1-score were also determined for each model. For performance evaluation, we also used calibration curve, the Brier score, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS Among the 6,914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was more likely to occur during the night shift compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality was higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.762, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787, which was obtained using RF. CLINICALTRIAL N/A

Download Full-text

Association of opioid exposure during intensive care unit stays with post-discharge opioid use: A retrospective study and literature review

Journal of Opioid Management ◽

10.5055/jom.2021.0685 ◽

2021 ◽

Vol 17 (6) ◽

pp. 511-516

Author(s):

Yoonsun Mo, MS, PharmD, BCPS, BCCCP ◽

John Zeibeq, MD ◽

Nabil Mesiha, MD ◽

Abou Bakar, PharmD ◽

Maram Sarsour, PharmD ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Retrospective Study ◽

Hospital Discharge ◽

Invasive Mechanical Ventilation ◽

High Dose ◽

Opioid Use ◽

Electronic Health Record Data ◽

Icu Patients ◽

Mechanically Ventilated

Objective: To evaluate whether pain management strategies within intensive care unit (ICU) settings contribute to chronic opioid use upon hospital discharge in opioid-naive patients requiring invasive mechanical ventilation. Design: A retrospective, observational study.Setting: An 18-bed mixed ICU at a community teaching hospital located in Brooklyn, New York.Participants: This study included mechanically ventilated patients requiring continuous opioid infusion from April 25, 2017 to May 16, 2019. Patients were excluded if they received chronic opioid therapy at home or expired during this hospital admission. Eligible patients were identified using an electronic health record data query.Main outcome measure(s): The proportion of ICU patients who continued to require opioids upon ICU and hospital discharge. Results: A total of 196 ICU patients were included in this study. Of these, 22 patients were transferred to a regular floor while receiving a fentanyl transdermal patch. However, the fentanyl patch treatment was continued only for three patients (2 percent) at hospital discharge.Conclusions: This retrospective study suggested that high-dose use of opioids in mechanically ventilated, opioid-naive ICU patients was not associated with continued opioid use upon hospital discharge.

Download Full-text

Acute kidney injury in pediatric patients: experience of a single center during an 11-year period

Medicina ◽

10.3390/medicina46080073 ◽

2010 ◽

Vol 46 (8) ◽

pp. 511 ◽

Cited By ~ 11

Author(s):

Birutė Pundzienė ◽

Diana Dobilienė ◽

Šarūnas Rudaitis

Keyword(s):

Intensive Care Unit ◽

Acute Kidney Injury ◽

Intensive Care ◽

Organ Dysfunction ◽

Multiple Organ Dysfunction Syndrome ◽

Kidney Injury ◽

Pediatric Intensive Care ◽

Multiple Organ ◽

Multiple Organ Dysfunction ◽

Second Period

The aim of our study was to determine the causes of acute kidney injury (AKI) in children, to compare outcomes between two periods – 1998–2003 and 2004-2008 – and to evaluate the influence of new methods of renal replacement therapy on mortality. Material and methods. A retrospective analysis of medical record data of all children treated for AKI at the Clinic of Children Diseases, Hospital of Kaunas University of Medicine, during the period of 1998–2008 was made. Both periods were compared regarding various variables. Results. Of the 179 children with AKI, 75 (41.9%) were treated during 1998–2003 and 104 (58.1%) during 2004–2008. Primary glomerular disease and sepsis were the leading causes of AKI in both the periods. AKI without involvement of other organs was diagnosed for 106 (59.2%) children: for 42 (56.0%) children in the first period and 64 (61.5%) in the second. A total of 124 (69.3%) children were treated in a pediatric intensive care unit. Multiple organ dysfunction syndrome with AKI was diagnosed for 33 (44%) patients in the first period and for 40 (38.5%) in the second. A significant decrease in mortality among patients with multiple organ dysfunction syndrome during the second period was observed (78.8% vs. 37.5%). Conclusions. More than half of patients had secondary acute kidney injury of nonrenal origin. More than two-thirds (69.3%) of patients with AKI were treated in the pediatric intensive care unit. Multiple organ dysfunction syndrome was diagnosed for 40.8% of children with AKI. Renal replacement therapy was indicated for one-third of patients with AKI. A 2.5-fold decrease in mortality was observed in the second period as compared to the first one.

Download Full-text

Acute kidney injury incidence and acute renal replacement modality in intensive care unit patients in Indonesia: A result from Southeast Asia-Acute Kidney Injury Study

10.21203/rs.2.24585/v1 ◽

2020 ◽

Author(s):

Jonny Jonny ◽

Moch Hasyim ◽

Vedora Angelia ◽

Ayu Nursantisuryani Jahya ◽

Lydia Permata Hilman ◽

...

Keyword(s):

Intensive Care Unit ◽

Acute Kidney Injury ◽

Intensive Care ◽

Renal Replacement Therapy ◽

Southeast Asia ◽

Replacement Therapy ◽

Kidney Injury ◽

Apache Ii Score ◽

Icu Patients ◽

Three Stages

Abstract Background : Currently, there is limited data of large databases of acute kidney injury (AKI) epidemiology from Southeast Asia, especially in Indonesia, the biggest countries in. Therefore, we aimed to provide demographic data of intensive care unit (ICU) patients with AKI and the utilization of renal replacement therapy (RRT) in Indonesia. Methods : We collected demographic and clinical data from 952 ICU patients. Patients were classified into AKI and non-AKI. AKI was classified according to the Kidney Disease Improving Global Outcome (KDIGO) criteria in three stages. We then assess the Acute Physiology and Chronic Health Evaluation (APACHE) II score of AKI and non-AKI patients. RRT modalities were listed down by the number of procedures conducted. Results : Overall incidence of AKI was 43%, distributed among three stages: 18.5 % stage 1, 33% stage 2, 48.5 % stage 3. Patients developing AKI need mechanical ventilation more often in comparison with non-AKI. Patients with AKI have an average APACHE score of 16.5, while non-AKI patients have an average score of 9.9. Among AKI patients, 24.6% requires RRT. The most common RRT modalities were intermittent hemodialysis (69.4%), followed by slow low efficiency dialysis (22.1%), continuous renal replacement therapy (4.2%), and peritoneal dialysis (1.1%). Conclusions: This study showed that AKI is a common problem in Indonesian ICU with containing a high mortality rate. We strongly believe that identification the risk factor of AKI will provide the opportunity to develop the predictability score for AKI prevention and finally improve AKI outcome.

Download Full-text

Association Between Base Excess and Mortality Among Patients in ICU with Acute Kidney Injury

10.21203/rs.3.rs-746329/v1 ◽

2021 ◽

Author(s):

Yi Cheng ◽

Yuanjun Tang ◽

Boxiang Tu ◽

Xin Cheng ◽

Ran Qi ◽

...

Keyword(s):

Acute Kidney Injury ◽

Medical Information ◽

Proportional Hazards ◽

Base Excess ◽

Proportional Hazards Model ◽

Kidney Injury ◽

Prognostic Indicator ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Icu Patients

Abstract Objective This study aimed to explore the association between base excess (BE) and risk of 30-day mortality among patients with acute kidney injury (AKI) in ICU.Methods This retrospective study including ICU patients with AKI from Medical Information Mart for Intensive Care (MIMIC)-IV database. We used multivariate Cox proportional-hazards model to calculate the hazard ratio (HR) for risk of 30-day mortality among patients with AKI. Furthermore, we utilized Cox proportional-hazard model with restrict cubic splines (RCS) to explore the potential no-linear association. Results Of all the 14238 ICU patients with AKI, BE showed U-shaped relationship with risk of 30-day mortality for patients with AKI, and higher or lower BE value could increase the risk. Compared with normal base excess (-3~3 mmol/L), patients with difference groups (BE ≤ -9mmol/L, -9 mmol/L <BE≤-3 mmol/L, 3 mmol/L <BE≤9 mmol/L and BE＞9 mmol/L) had different HR for mortality: 1.57(1.40,1.76), 1.26(1.14,1.39), 0.97(0.83,1.12), 1.53(1.17,2.02) respectively. And the RCS analyses also showed U-shaped curve between BE and 30-day mortality risk.Conclusion Our results suggest both higher and lower BE in patients with AKI would increase the risk of 30-day mortality. BE measured at administration could be a critical prognostic indicator for ICU patients with AIK and provide guidance for clinicians.

Download Full-text

Neutrophil-to-lymphocyte ratio as a predictor of mortality in intensive care unit patients: a retrospective analysis of the Medical Information Mart for Intensive Care III Database

BMJ Open ◽

10.1136/bmjopen-2021-053548 ◽

2021 ◽

Vol 11 (11) ◽

pp. e053548

Author(s):

Xie Wu ◽

Qipeng Luo ◽

Zhanhao Su ◽

Yinan Li ◽

Hongbai Wang ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Inflammatory Markers ◽

Predictive Power ◽

Medical Information ◽

Predictive Ability ◽

Scoring Systems ◽

Mortality Rates ◽

Simplified Acute Physiology Score ◽

Icu Patients

ObjectivesIdentifying high-risk patients in the intensive care unit (ICU) is important given the high mortality rate. However, existing scoring systems lack easily accessible, low-cost and effective inflammatory markers. We aimed to identify inflammatory markers in routine blood tests to predict mortality in ICU patients and evaluate their predictive power.DesignRetrospective case–control study.SettingSingle secondary care centre.ParticipantsWe analysed data from the Medical Information Mart for Intensive Care III database. A total of 21 822 ICU patients were enrolled and divided into survival and death groups based on in-hospital mortality.Primary and secondary outcome measuresThe predictive values of potential inflammatory markers were evaluated and compared using receiver operating characteristic curve analysis. After identifying the neutrophil-to-lymphocyte ratio (NLR) as having the best predictive ability, patients were redivided into low (≤1), medium (1–6) and high (>6) NLR groups. Univariate and multivariate logistic regression analyses were performed to evaluate the association between the NLR and mortality. The area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess whether incorporating the NLR could improve the predictive power of existing scoring systems.ResultsThe NLR had the best predictive ability (AUC: 0.609; p<0.001). In-hospital mortality rates were significantly higher in the low (OR (OR): 2.09; 95% CI 1.64 to 2.66) and high (OR 1.64; 95% CI 1.50 to 1.80) NLR groups than in the medium NLR group. Adding the NLR to the Simplified Acute Physiology Score II improved the AUC from 0.789 to 0.798, with an NRI and IDI of 16.64% and 0.27%, respectively.ConclusionsThe NLR predicted mortality in ICU patients well. Both low and high NLRs were associated with elevated mortality rates, including the NLR may improve the predictive power of the Simplified Acute Physiology Score II.

Download Full-text

Withdrawal of Life-Sustaining Therapy in Intensive Care Unit Patients Following Out-Of-Hospital Cardiac Arrest: An Australian Metropolitan ICU Experience

10.21203/rs.3.rs-1215202/v1 ◽

2022 ◽

Author(s):

Nilesh Anand Devanand ◽

Mohammed Ishaq Ruknuddeen ◽

Natalie Soar ◽

Suzanne Edwards

Keyword(s):

Intensive Care Unit ◽

Cardiac Arrest ◽

Intensive Care ◽

Length Of Stay ◽

Motor Response ◽

Temperature Management ◽

Icu Patients ◽

Icu Length Of Stay ◽

Clinical Records ◽

Hospital Cardiac Arrest

Abstract Objective: To determine factors associated with withdrawal of life-sustaining therapy (WLST) in intensive care unit (ICU) patients following out-of-hospital cardiac arrest (OHCA).Methods: A retrospective review of ICU data from patient clinical records following OHCA was conducted from January 2010 to December 2015. Demographic features, cardiac arrest characteristics, clinical attributes and targeted temperature management were compared between patients with and without WLST. We dichotomised WLST into early (ICU length of stay <72 hours) and late (ICU length of stay ≥72 hours). Factors independently associated with WLST were determined by multivariable binary logistic regression using a backward elimination method, and results were depicted as odds ratios (OR) with 95% confidence intervals (CI).Results: The study selection criteria resulted in a cohort of 260 ICU patients post-OHCA, with a mean age of 58 years and the majority were males (178, 68%); 151 patients (58%) died, of which 145 (96%) underwent WLST, with the majority undergoing early WLST (89, 61%). Status myoclonus was the strongest independent factor associated with early WLST (OR 38.90, 95% CI 4.55–332.57; p < 0.001). Glasgow Coma Scale (GCS) motor response of <4 on day 3 post-OHCA was the strongest factor associated with delayed WLST (OR 91.59, 95% CI 11.66–719.18; p < 0.0001).Conclusion: The majority of deaths in ICU patients post-OHCA occurred following early WLST. Status myoclonus and a GCS motor response of <4 on day 3 post-OHCA are independently associated with WLST.

Download Full-text