Identifying key predictors of mortality in young patients on chronic haemodialysis—a machine learning approach

Abstract Background The mortality risk remains significant in paediatric and adult patients on chronic haemodialysis (HD) treatment. We aimed to identify factors associated with mortality in patients who started HD as children and continued HD as adults. Methods The data originated from a cohort of patients <30 years of age who started HD in childhood (≤19 years) on thrice-weekly HD in outpatient DaVita dialysis centres between 2004 and 2016. Patients with at least 5 years of follow-up since the initiation of HD or death within 5 years were included; 105 variables relating to demographics, HD treatment and laboratory measurements were evaluated as predictors of 5-year mortality utilizing a machine learning approach (random forest). Results A total of 363 patients were included in the analysis, with 84 patients having started HD at <12 years of age. Low albumin and elevated lactate dehydrogenase (LDH) were the two most important predictors of 5-year mortality. Other predictors included elevated red blood cell distribution width or blood pressure and decreased red blood cell count, haemoglobin, albumin:globulin ratio, ultrafiltration rate, z-score weight for age or single-pool Kt/V (below target). Mortality was predicted with an accuracy of 81%. Conclusions Mortality in paediatric and young adult patients on chronic HD is associated with multifactorial markers of nutrition, inflammation, anaemia and dialysis dose. This highlights the importance of multimodal intervention strategies besides adequate HD treatment as determined by Kt/V alone. The association with elevated LDH was not previously reported and may indicate the relevance of blood–membrane interactions, organ malperfusion or haematologic and metabolic changes during maintenance HD in this population.

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Red Blood Cell Volume Distribution Width (RDW) in Uraemic Patients on Chronic Haemodialysis

The International Journal of Artificial Organs ◽

10.1177/039139888901200307 ◽

1989 ◽

Vol 12 (3) ◽

pp. 170-174 ◽

Cited By ~ 13

Author(s):

D. Docci ◽

C. Delvecchio ◽

C. Gollini ◽

F. Turci ◽

L. Baldrati ◽

...

Keyword(s):

Blood Cell ◽

Cell Volume ◽

Red Blood Cell ◽

Iron Status ◽

Volume Distribution ◽

Chronic Haemodialysis ◽

Distribution Width ◽

Iron Administration ◽

Parallel Increase ◽

Normal Individuals

Red blood cell volume distribution width (RDW) was obtained with the Coulter counter in 60 haemodialysis patients and 55 normal individuals. RDW tended to be higher in the former and the degree of increase was to some extent correlated with the underlying nephropathy. Although RDW failed to correlate with conventional tests of iron status, it was observed that iron administration could produce a decrease toward normal in RDW and a parallel increase in haemoglobin when the initial RDW was increased. In contrast, the response to iron was negligible in the patients with normal RDW basally. It was concluded that high RDW is an acceptable indicator of iron deficiency in haemodialysis patients.

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Machine-Learning Approach for Modeling Myelosuppression Attributed to Nimustine Hydrochloride

JCO Clinical Cancer Informatics ◽

10.1200/cci.17.00022 ◽

2018 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Takuma Shibahara ◽

Soko Ikuta ◽

Yoshihiro Muragaki

Keyword(s):

Machine Learning ◽

Adverse Effect ◽

Blood Cell ◽

Support Vector ◽

Learning Approach ◽

Who Grade ◽

Hematopoietic Stem ◽

Cell Counts ◽

Machine Learning Approach ◽

Nimustine Hydrochloride

Purpose A major adverse effect arising from nimustine hydrochloride (ACNU) therapy for brain tumors is myelosuppression. Because its timing and severity vary among individual patients, the ACNU dose level has been adjusted in an empiric manner at individual medical facilities. To our knowledge, ours is the first study to develop a machine-learning approach to estimate myelosuppression through analysis of patient factors before treatment and attempts to clarify the relationship between myelosuppression and hematopoietic stem cells from daily clinical data. Adverse effect prediction will allow ACNU dose adjustment for patients predicted to have decreases in blood cell counts and will enable focused follow-up of patients undergoing chemoradiotherapy. Patients and Methods Patients were newly pathologically diagnosed with WHO grade 2 or 3 tumors and were treated with ACNU-based chemoradiotherapy. For detailed analysis of the timing and intensity of adverse effects in patients, we developed a data-weighted support vector machine (SVM) based on adverse event criteria (nadir-weighted SVM [NwSVM]). To evaluate the estimation accuracy of blood cell count dynamics, the determination coefficient ( r2) between real and estimated data was calculated by three regression methods: polynomial, SVM, and NwSVM. Results Only the NwSVM-based regression enabled estimation of the dynamics of all blood cell types with high accuracy (mean r2 = 0.81). The mean timing of nadir arrival estimated using this regression was 35 days for platelets, 41 days for RBCs, 52 days for lymphocytes, 57 days for WBCs, and 62 days for neutrophils. Conclusion The NwSVM can be used to predict myelosuppression and clearly depicts nadir timing differences between platelets and other blood cells.

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A Machine Learning Approach for the Prediction of Traumatic Brain Injury Induced Coagulopathy

Frontiers in Medicine ◽

10.3389/fmed.2021.792689 ◽

2021 ◽

Vol 8 ◽

Author(s):

Fan Yang ◽

Chi Peng ◽

Liwei Peng ◽

Jian Wang ◽

Yuejun Li ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Intensive Care ◽

Brain Injury ◽

Blood Cell ◽

Red Blood Cell ◽

Research Database ◽

Distribution Width ◽

Predictive Values ◽

Extreme Gradient Boosting

Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

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