Implementation of single source based hospital information system for the catholic medical center affiliated hospitals

Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model

BACKGROUND

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of this study was to develop a deep learning-based artificial intelligence (AI) model to predict IDH using pre-dialysis features.

METHODS

Data from 2007 patients with 943 220 HD sessions at seven university hospitals were used. The performance of the deep learning model was compared with three machine learning models (logistic regression, random forest and XGBoost).

RESULTS

IDH occurred in 5.39% of all studied HD sessions. A lower pre-dialysis blood pressure (BP), and a higher ultrafiltration (UF) target rate and interdialytic weight gain in IDH sessions compared with non-IDH sessions, and the occurrence of IDH in previous sessions was more frequent among IDH sessions compared with non-IDH sessions. Matthews correlation coefficient and macro-averaged F1 score were used to evaluate both positive and negative prediction performances. Both values were similar in logistic regression, random forest, XGBoost and deep learning models, developed with data from a single session. When combining data from the previous three sessions, the prediction performance of the deep learning model improved and became superior to that of other models. The common top-ranked features for IDH prediction were mean systolic BP (SBP) during the previous session, UF target rate, pre-dialysis SBP, and IDH experience during the previous session.

CONCLUSIONS

Our AI model predicts IDH accurately, suggesting it as a reliable tool for HD treatment.

High C-reactive protein to albumin ratio and the short-term survival prognosis within 30 days in terminal cancer patients receiving palliative care in a hospital setting: A retrospective analysis

Survival estimates are very important to patients with terminal cancer. The C-reactive protein (CRP)/albumin ratio is associated with cancer outcomes. However, few studies have investigated the dose-response association in terminal cancer patients. Therefore, we aimed to evaluate the association between the CRP/albumin ratio and mortality in terminal cancer patients using a longitudinal analysis. We retrospectively investigated the electronic medical records of 435 inpatients with terminal cancer admitted to the palliative care unit of Yeouido St. Mary's Hospital between October 8, 2015, and January 17, 2018. In total, 382 patients with terminal cancer were enrolled in the study. The serum CRP/albumin ratio measured at admission had a linear dose-response relationship with the risk of death among the terminal cancer patients (P for linearity = .011). The multivariate analyses showed that the CRP/albumin ratio was an independent prognostic factor (Model 1, CRP/albumin ratio >48.53 × 10: HR = 2.68, 95% CI = 1.82-3.93; Model 2, tertile 2: HR = 1.91, 95% CI = 1.31-2.82 and tertile 3: HR = 3.66, 95% CI = 2.24-5.97). The relationship between a high CRP/albumin ratio and poor survival was a flat L-shape for survival time with an inflection point at approximately 15 days, while the relationship was not significant in terminal cancer patients who survived beyond 30 days. This study demonstrated that high CRP/albumin ratios are significantly and independently associated with the short-term survival prognosis of terminal cancer patients within 30 days.

Discovery of prostate specific antigen pattern to predict castration resistant prostate cancer of androgen deprivation therapy

BACKGROUND

Prostate specific antigen (PSA) is an important biomarker to monitor the response to the treatment, but has not been fully utilized as a whole sequence. We used a longitudinal biomarker PSA to discover a new prognostic pattern that predicts castration-resistant prostate cancer (CRPC) after androgen deprivation therapy.

METHODS

We transformed the longitudinal PSA into a discrete sequence, used frequent sequential pattern mining to find candidate patterns from the sequences, and selected the most predictive and informative pattern among the candidates.

RESULTS

Patients were less likely to be CRPC if, after PSA values reach nadir, the PSA decreases more than 0.048 ng/ml during a month, and the decrease occurs again. This pattern significantly increased the accuracy of predicting CRPC by supplementing information provided by existing PSA patterns such as pretreatment PSA.

CONCLUSIONS

This result can help clinicians to stratify men by the risk of CRPC and to determine the patient that needs intensive follow-up.

Asan medical information system for healthcare quality improvement

Objectives

This purpose of this paper is to introduce the status of the Asan Medical Center (AMC) medical information system with respect to healthcare quality improvement.

Methods

Asan Medical Information System (AMIS) is projected to become a completely electronic and digital information hospital. AMIS has played a role in improving the health care quality based on the following measures: safety, effectiveness, patient-centeredness, timeliness, efficiency, privacy, and security.

Results

AMIS consisted of several distinctive systems: order communication system, electronic medical record, picture archiving communication system, clinical research information system, data warehouse, enterprise resource planning, IT service management system, and disaster recovery system. The most distinctive features of AMIS were the high alert-medication recognition & management system, the integrated and severity stratified alert system, the integrated patient monitoring system, the perioperative diabetic care monitoring and support system, and the clinical indicator management system.

Conclusions

AMIS provides IT services for AMC, 7 affiliated hospitals and over 5,000 partners clinics, and was developed to improve healthcare services. The current challenge of AMIS is standard and interoperability. A global health IT strategy is needed to get through the current challenges and to provide new services as needed.