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Lai B, Shen L, Ye S, Shen X, Zhou D, Guo X, Zhou H, Pan Y, Tong J. Influence of continuity of care on self-management ability and quality of life in outpatient maintenance hemodialysis patients. Ther Apher Dial 2022; 26:1166-1173. [PMID: 35043556 PMCID: PMC9790337 DOI: 10.1111/1744-9987.13800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 10/26/2021] [Accepted: 12/02/2021] [Indexed: 12/30/2022]
Abstract
INTRODUCTION The objective of this study was to evaluate the effect of continuity of care on self-management ability and quality of life (QOL) in patients undergoing maintenance hemodialysis (MHD). METHODS One hundred patients were randomly assigned to the observation group and the control group. In the observation group, patients received a 12-month continuity of care. In the control group, patients were given with routine nursing. Evaluate the patients' self-management ability and QOL between two groups 1 week before discharge and 6 and 12 months outpatient MHD. RESULTS Observation group had higher Hemodialysis Self-Management Instrument (HD-SMI) scores and Kidney Disease Quality of Life-Short Form (KDQOL-SF™) scores than control group at 6 and 12 months outpatient MHD. But patients in observation group had a much lower systolic blood pressure than those in control group at 12 months outpatient MHD. CONCLUSIONS Our study suggested that continuity of care in the form of online education, telephone visit, and outpatient visit could improve self-management ability and QOL of patients undergoing MHD.
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Affiliation(s)
- Bihong Lai
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Li Shen
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Shuiying Ye
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Xia Shen
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Dongchi Zhou
- Department of NephrologyShanghai Pudong Hospital, Fudan University Pudong Medical CenterShanghaiChina
| | - Xiaocui Guo
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Huaxian Zhou
- Department of Nursing, Shanghai Pudong HospitalFudan University Pudong Medical CenterShanghaiChina
| | - Yangbin Pan
- Department of NephrologyShanghai Pudong Hospital, Fudan University Pudong Medical CenterShanghaiChina
| | - Jindong Tong
- Department of Vascular SurgeryShanghai Pudong Hospital, Fudan University Pudong Medical CenterShanghaiChina
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Chua HR, Zheng K, Vathsala A, Ngiam KY, Yap HK, Lu L, Tiong HY, Mukhopadhyay A, MacLaren G, Lim SL, Akalya K, Ooi BC. Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study. J Med Internet Res 2021; 23:e30805. [PMID: 34951595 PMCID: PMC8742216 DOI: 10.2196/30805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 09/26/2021] [Accepted: 10/29/2021] [Indexed: 12/29/2022] Open
Abstract
Background Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. Objective The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. Methods The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter’s corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. Results The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. Conclusions We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.
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Affiliation(s)
- Horng-Ruey Chua
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kaiping Zheng
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Anantharaman Vathsala
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kee-Yuan Ngiam
- Division of Endocrine Surgery, Department of Surgery, National University Hospital, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hui-Kim Yap
- Division of Paediatric Nephrology, Department of Paediatrics, National University Children's Medical Institute, Singapore, Singapore.,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Liangjian Lu
- Division of Paediatric Nephrology, Department of Paediatrics, National University Children's Medical Institute, Singapore, Singapore.,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ho-Yee Tiong
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Urology, National University Hospital, Singapore, Singapore
| | - Amartya Mukhopadhyay
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Graeme MacLaren
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Cardiothoracic Intensive Care Unit, Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, Singapore, Singapore
| | - Shir-Lynn Lim
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Cardiology, National University Heart Centre, Singapore, Singapore
| | - K Akalya
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Beng-Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
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Chua HR, Low S, Murali TM, Wong ETY, He HD, Teo BW, Thian YL, Akalya K, Vathsala A. Cumulative iodinated contrast exposure for computed tomography during acute kidney injury and major adverse kidney events. Eur Radiol 2020; 31:3258-3266. [PMID: 33159575 DOI: 10.1007/s00330-020-07428-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/06/2020] [Accepted: 10/14/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To determine if contrast-enhanced CT imaging performed in patients during their episode of AKI contributes to major adverse kidney events (MAKE). METHODS A propensity score-matched analysis of 1127 patients with AKI defined by KDIGO criteria was done. Their mean age was 63 ± 16 years with 56% males. A total of 419 cases exposed to CT contrast peri-AKI were matched with 798 non-exposed controls for 14 covariates including comorbidities, acute illnesses, and initial AKI severity; outcomes including MAKE and renal recovery in hospital were compared using bivariate analysis and logistic regression. MAKE was a composite of mortality, renal replacement therapy, and doubling of serum creatinine on discharge over baseline; renal recovery was classified as early versus late based on a 7-day timeline from AKI onset to nadir creatinine or cessation of renal replacement therapy in survivors. RESULTS Sixty-two patients received cumulatively > 100 mL of CT contrast, 143 patients had > 50-100 mL, and 214 patients had 50 mL or less; MAKE occurred in 34%, 17%, and 21%, respectively, as compared with 20% in non-exposed controls (p = 0.008 for patients with > 100 mL contrast versus none). More contrast-exposed patients experienced late renal recovery (27% versus 20%) and longer hospital days (median 10 versus 8) than non-exposed patients (all p < 0.01). On multivariate analysis, cumulative CT contrast > 100 mL was independently associated with MAKE (odds ratio 2.39 versus non-contrast, adjusted for all confounders, p = 0.005); cumulative CT contrast under 100 mL was not associated with MAKE. CONCLUSIONS High cumulative volume of CT contrast administered to patients with AKI is associated with worse short-term renal outcomes and delayed renal recovery. KEY POINTS • Cumulative intravenous iodinated contrast for CT imaging of more than 100 mL, during an episode of acute kidney injury, was independently associated with worse renal outcomes and less renal recovery. • These adverse outcomes including renal replacement therapy were not more frequent in similar patients who received cumulatively 100 mL or less of CT contrast, compared with non-exposed patients. • More patients with CT contrast exposure during acute kidney injury experienced delayed renal recovery.
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Affiliation(s)
- Horng-Ruey Chua
- Division of Nephrology, Department of Medicine, National University Hospital, Level 10, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Singapore. .,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Sanmay Low
- Division of Renal Medicine, Department of Medicine, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Tanusya Murali Murali
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Emmett Tsz-Yeung Wong
- Division of Nephrology, Department of Medicine, National University Hospital, Level 10, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hai-Dong He
- Department of Nephrology, Shanghai Minhang District Central Hospital, Shanghai, People's Republic of China
| | - Boon-Wee Teo
- Division of Nephrology, Department of Medicine, National University Hospital, Level 10, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yee-Liang Thian
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - K Akalya
- Division of Nephrology, Department of Medicine, National University Hospital, Level 10, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Singapore
| | - Anantharaman Vathsala
- Division of Nephrology, Department of Medicine, National University Hospital, Level 10, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Chua HR, MacLaren G, Choong LHL, Chionh CY, Khoo BZE, Yeo SC, Sewa DW, Ng SY, Choo JCJ, Teo BW, Tan HK, Siow WT, Agrawal RV, Tan CS, Vathsala A, Tagore R, Seow TYY, Khatri P, Hong WZ, Kaushik M. Ensuring Sustainability of Continuous Kidney Replacement Therapy in the Face of Extraordinary Demand: Lessons From the COVID-19 Pandemic. Am J Kidney Dis 2020; 76:392-400. [PMID: 32505811 PMCID: PMC7272152 DOI: 10.1053/j.ajkd.2020.05.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 05/27/2020] [Indexed: 01/08/2023]
Abstract
With the exponential surge in patients with coronavirus disease 2019 (COVID-19) worldwide, the resources needed to provide continuous kidney replacement therapy (CKRT) for patients with acute kidney injury or kidney failure may be threatened. This article summarizes subsisting strategies that can be implemented immediately. Pre-emptive weekly multicenter projections of CKRT demand based on evolving COVID-19 epidemiology and routine workload should be made. Corresponding consumables should be quantified and acquired, with diversification of sources from multiple vendors. Supply procurement should be stepped up accordingly so that a several-week stock is amassed, with administrative oversight to prevent disproportionate hoarding by institutions. Consumption of CKRT resources can be made more efficient by optimizing circuit anticoagulation to preserve filters, extending use of each vascular access, lowering blood flows to reduce citrate consumption, moderating the CKRT intensity to conserve fluids, or running accelerated KRT at higher clearance to treat more patients per machine. If logistically feasible, earlier transition to intermittent hemodialysis with online-generated dialysate, or urgent peritoneal dialysis in selected patients, may help reduce CKRT dependency. These measures, coupled to multicenter collaboration and a corresponding increase in trained medical and nursing staffing levels, may avoid downstream rationing of care and save lives during the peak of the pandemic.
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Affiliation(s)
- Horng-Ruey Chua
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Graeme MacLaren
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cardiothoracic Intensive Care Unit, Department of Cardiac, Thoracic & Vascular Surgery, National University Hospital, Singapore
| | - Lina Hui-Lin Choong
- Department of Renal Medicine, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore
| | - Chang-Yin Chionh
- Department of Renal Medicine, Changi General Hospital, Singapore
| | | | - See-Cheng Yeo
- Department of Renal Medicine, Tan Tock Seng Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Duu-Wen Sewa
- Duke-NUS Medical School, Singapore; Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore
| | - Shin-Yi Ng
- Duke-NUS Medical School, Singapore; Department of Surgical Intensive Care, Singapore General Hospital, Singapore
| | - Jason Chon-Jun Choo
- Department of Renal Medicine, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore
| | - Boon-Wee Teo
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Han-Khim Tan
- Department of Renal Medicine, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore
| | - Wen-Ting Siow
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore
| | - Rohit Vijay Agrawal
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Anaesthesia, National University Hospital, Singapore
| | - Chieh-Suai Tan
- Department of Renal Medicine, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore
| | - Anantharaman Vathsala
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Rajat Tagore
- Division of Renal Medicine, Department of Medicine, Ng Teng Fong General Hospital, Singapore
| | - Terina Ying-Ying Seow
- Division of Renal Medicine, Department of Medicine, Sengkang General Hospital, Singapore
| | - Priyanka Khatri
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore; Fast and Chronic Programmes, Alexandra Hospital, Singapore
| | - Wei-Zhen Hong
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore; Fast and Chronic Programmes, Alexandra Hospital, Singapore
| | - Manish Kaushik
- Department of Renal Medicine, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore.
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