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Journal of Peking University (Health Sciences) ›› 2022, Vol. 54 ›› Issue (3): 458-467. doi: 10.19723/j.issn.1671-167X.2022.03.010

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Long short-term memory and Logistic regression for mortality risk prediction of intensive care unit patients with stroke

Yu-han DENG1,Yong JIANG2,3,Zi-yao WANG1,Shuang LIU1,Yu-xin WANG1,Bao-hua LIU1,*()   

  1. 1. Department of Social Medicine and Health Education, Peking University School of Public Health, Beijing 100191, China
    2. China National Clinical Research Center for Neurological Diseases, Department of Neurology, Beijing Tian Tan Hospital, Capital Medical University, Beijing 100050, China
    3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine (Beihang University & Capital Medical University), Beijing 100070, China
  • Received:2022-01-13 Online:2022-06-18 Published:2022-06-14
  • Contact: Bao-hua LIU E-mail:baohualiu@bjmu.edu.cn
  • Supported by:
    National Key R & D Program of China(2018YFC1311700);National Key R & D Program of China(2018YFC1311703)

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Abstract:

Objective: To select variables related to mortality risk of stroke patients in intensive care unit (ICU) through long short-term memory (LSTM) with attention mechanisms and Logistic regression with L1 norm, and to construct mortality risk prediction model based on conventional Logistic regression with important variables selected from the two models and to evaluate the model performance. Methods: Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database was retrospectively analyzed and the patients who were primarily diagnosed with stroke were selected as study population. The outcome was defined as whether the patient died in hospital after admission. Candidate predictors included demogra-phic information, complications, laboratory tests and vital signs in the initial 48 h after ICU admission. The data were randomly divided into a training set and a test set for ten times at a ratio of 8 ∶2. In training sets, LSTM with attention mechanisms and Logistic regression with L1 norm were constructed to select important variables. In the test sets, the mean importance of variables of ten times was used as a reference to pick out the top 10 variables in each of the two models, and then these variables were included in conventional Logistic regression to build the final prediction model. Model evaluation was based on the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. And the model performance was compared with the forward Logistic regression model which hadn't conducted variable selection previously. Results: A total of 2 755 patients with 2 979 ICU admission records were included in the analysis, of which 526 recorded deaths. The AUC of Logistic regression model with L1 norm was statistically better than that of LSTM with attention mechanisms (0.819±0.031 vs. 0.760±0.018, P < 0.001). Age, blood glucose, and blood urea nitrogen were at the top ten important variables in both of the two models. AUC, sensitivity, specificity, and accuracy of Logistic regression models were 0.85, 85.98%, 71.74% and 74.26%, respectively. And the final prediction model was superior to forward Logistic regression model. Conclusion: The variables selected by Logistic regression with L1 norm and LSTM with attention mechanisms had good prediction performance, which showed important implications on the mortality prediction of stroke patients in ICU.

Key words: Stroke, Prognosis, Forecasting, LSTM, Logistic models

CLC Number: 

  • R743.3

Figure 1

Flow chart of inclusion of study population MIMIC-Ⅳ, Medical Information Mart for Intensive Care-Ⅳ; ICU, intensive care unit."

Figure 2

Diagram of an LSTM cell LSTM, long short-term memory. It, input gate; Ft, forget gate; Ot, output gate; $\tilde{C}_{t}$, candidate memory cell; Ct, memory cell; Ht, hidden layer; Xt, input variables; Ct-1, memory cell at last time step; Ht-1, hidden layer at last time step."

Table 1

Characteristics of stroke patients in ICU grouped by outcome"

Characteristic Total (n=2 979) Outcome
Death (n=526) Survival (n=2 453) P value
Age/years 69 (56, 80) 75 (64, 83) 67 (55, 79) < 0.001
Gender 0.737
  Male 1 532 (51.4) 274 (52.1) 1 258 (51.3)
  Female 1 447 (48.6) 252 (47.9) 1 195 (48.7)
Ethnicity < 0.001
  White 1 784 (59.9) 251 (47.7) 1 533 (62.5)
  Black American 306 (10.3) 61 (11.6) 245 (10.0)
  Asian 112 (3.8) 23 (4.4) 89 (3.6)
  Hispanic 111 (3.7) 15 (2.9) 96 (3.9)
  Others/unknown 666 (22.4) 176 (33.5) 490 (20.0)
Stroke subtype 0.270
  Ischemic stroke 1 185 (39.8) 198 (37.6) 987 (40.2)
  Hemorrhagic stroke 1 794 (60.2) 328 (62.4) 1 466 (59.8)
AFIB < 0.001
  Yes 367 (12.3) 106 (20.2) 261 (10.6)
  No 2 612 (87.7) 420 (79.8) 2 192 (89.4)
CHF < 0.001
  Yes 216 (7.3) 65 (12.4) 151 (6.2)
  No 2 763 (92.7) 461 (87.6) 2 302 (93.8)
COPD 0.770
  Yes 212 (7.1) 39 (7.4) 173 (7.1)
  No 2 767 (92.9) 487 (92.6) 2 280 (92.9)
Diabetes < 0.001
  Yes 333 (11.2) 82 (15.6) 251 (10.2)
  No 2 646 (88.8) 444 (84.4) 2 202 (89.8)
Hyperlipidemia 0.055
  Yes 1 181 (39.6) 189 (35.9) 992 (40.4)
  No 1 798 (60.4) 337 (64.1) 1 461 (59.6)
Hypertension 0.002
  Yes 949 (31.9) 137 (26.0) 812 (33.1)
  No 2 030 (68.1) 389 (74.0) 1 641 (66.9)
Liver disease < 0.001
  Yes 120 (4.0) 39 (7.4) 81 (3.3)
  No 2 859 (96.0) 487 (92.6) 2 372 (96.7)
PVD 0.052
  Yes 77 (2.6) 20 (3.8) 57 (2.3)
  No 2 902 (97.4) 506 (96.2) 2 396 (97.7)
Rental disease < 0.001
  Yes 526 (17.7) 141 (26.8) 385 (15.7)
  No 2 453 (82.3) 385 (73.2) 2 068 (84.3)
Respiratory failure < 0.001
  Yes 814 (27.3) 278 (52.9) 536 (21.9)
  No 2 165 (72.7) 248 (47.1) 1 917 (78.1)
HR/(beats/min) 79.3 (70.5, 88.2) 84.8 (75.7, 94.5) 78.2 (69.5, 86.8) < 0.001
Respiratory rate/(times/min) 18.6 (16.7, 20.9) 19.9 (17.7, 22.4) 18.4 (16.6, 20.5) < 0.001
SpO2/% 97.2 (96.0, 98.4) 98.0 (96.6, 99.0) 97.0 (95.9, 98.2) < 0.001
MBP/mmHg 85.9 (77.7, 92.6) 83.9 (75.6, 90.0) 86.4 (78.1, 93.3) < 0.001
DBP/mmHg 70.7 (62.5, 77.3) 68.6 (59.7, 75.0) 71.1 (63.2, 77.9) < 0.001
SBP/mmHg 130.9 (120.6, 140.7) 130.9 (120.0, 140.0) 130.9 (120.7, 140.9) 0.286
PT/s 12.9 (12.1, 13.6) 13.3 (12.4, 14.2) 12.8 (12.0, 13.4) < 0.001
INR 1.2 (1.1, 1.2) 1.2 (1.1, 1.3) 1.2 (1.1, 1.2) < 0.001
RDW/% 13.9 (13.3, 14.7) 14.2 (13.6, 15.3) 13.8 (13.2, 14.6) < 0.001
Hemoglobin/(g/dL) 11.8 (10.6, 12.9) 11.5 (10.3, 12.5) 11.8 (10.7, 12.9) < 0.001
WBC/(×109/L) 10.4 (8.7, 12.7) 11.8 (9.8, 15.0) 10.2 (8.6, 12.3) < 0.001
Platelet count/(×109/L) 207.6 (172.6, 248.0) 196.2 (157.0, 240.2) 209.9 (175.1, 249.9) < 0.001
Hematocrit/% 35.5 (32.3, 38.5) 34.7 (31.3, 37.7) 36.7 (32.6, 38.6) < 0.001
Glucose/(mg/dL) 127.4 (112.3, 147.2) 141.5 (124.5, 168.9) 124.8 (110.8, 142.5) < 0.001
Anion gap/(mmol/L) 14.0 (12.6, 15.4) 14.4 (12.9, 16.2) 13.9 (12.6, 15.3) < 0.001
Bicarbonate/(mmol/L) 23.2 (21.7, 24.9) 22.8 (20.8, 24.8) 23.3 (21.9, 24.9) < 0.001
Creatinine/(mg/dL) 0.9 (0.7, 1.1) 1.0 (0.8, 1.3) 0.9 (0.7, 1.0) < 0.001
Urea nitrogen/(mg/dL) 15.6 (12.2, 20.8) 18.8 (13.9, 26.4) 15.0 (11.9, 19.8) < 0.001
Chloride/(mmol/L) 105.6 (103.1, 107.8) 106.9 (103.9, 110.8) 105.3 (103.0, 107.4) < 0.001
Potassium/(mmol/L) 3.9 (3.7, 4.1) 3.9 (3.7, 4.2) 3.9 (3.7, 4.1) 0.011
Sodium/(mmol/L) 140.4 (138.5, 142.4) 141.5 (139.1, 145.2) 140.2 (138.4, 142.1) < 0.001

Figure 3

Receiver operating characteristic curves of two variable selection models LSTM, long short-term memory; LR, Logistic regression; AUC, area under curve."

Figure 4

Variable importance generated by LSTM with attention mechanisms LSTM, long short-term memory; CHF, congestive heart failure; AFIB, atrial fibrillation; WBC, white blood cells; RDW, red cell volume distribution width."

Figure 5

Variable importance based on Logistic regression with L1 norm HR, heart rate; WBC, white blood cells; RR, respiratory rate; PT, prothrombin time; SD, standard deviation."

Table 2

Prediction model based on Logistic regression"

Variable β SE Wald χ2 OR (95%CI)
Intercept -8.07 1.06 58.50
Age 3.42 0.38 82.52 30.81 (14.71, 64.55)
Ethnicity (ref: White)
  Black American -0.08 0.18 0.21 1.11 (0.74, 1.69)
  Asian -0.19 0.29 0.44 1.00 (0.49, 2.05)
  Hispanic -0.01 0.24 0.01 1.19 (0.66, 2.15)
  Others/unknown 0.48 0.14 12.38 1.96 (1.47, 2.60)
Hemoglobin (SD) -1.08 0.61 3.11 0.34 (0.10, 1.13)
Glucose (mean) 3.33 0.56 35.01 27.95 (9.28, 84.24)
Anion gap (mean) 2.24 0.78 8.38 9.43 (2.06, 43.07)
Bicarbonate (mean) -1.37 0.60 5.18 0.25 (0.08, 0.83)
Bicarbonate (SD) 0.77 0.48 2.56 2.17 (0.84, 5.59)
Urea nitrogen (SD) -0.98 0.71 1.89 0.37 (0.09, 1.52)
Sodium (max) 5.59 0.67 69.05 268.61 (71.81, 1 000.00)
SpO2 (mean) 3.72 1.55 5.80 41.37 (2.00, 855.38)
Heart rate (min) 0.54 0.59 0.81 1.71 (0.53, 5.49)
Hyperlipidemia (ref: no) -0.24 0.07 13.83 0.62 (0.48, 0.80)
CHF (ref: no) 0.25 0.10 6.13 1.66 (1.11, 2.48)
Liver disease (ref: no) 0.50 0.13 15.90 2.73 (1.67, 4.47)
Respiratory failure (ref: no) 0.55 0.06 74.68 3.02 (2.35, 3.89)

Figure 6

Receiver operating characteristic curves of two Logistic regression models LR, Logistic regression; AUC, area under curve."

1 Katan M , Luft A . Global burden of stroke[J]. Semin Neurol, 2018, 38 (2): 208- 211.
doi: 10.1055/s-0038-1649503
2 Rochmah TN , Rahmawati IT , Dahlui M , et al. Economic burden of stroke disease: A systematic review[J]. Int J Environ Res Public Health, 2021, 18 (14): 7552.
doi: 10.3390/ijerph18147552
3 Sarti C , Rastenyte D , Cepaitis Z , et al. International trends in mortality from stroke, 1968 to 1994[J]. Stroke, 2000, 31 (7): 1588- 1601.
doi: 10.1161/01.STR.31.7.1588
4 Handschu R , Haslbeck M , Hartmann A , et al. Mortality prediction in critical care for acute stroke: Severity of illness-score or coma-scale?[J]. J Neurol, 2005, 252 (10): 1249- 1254.
doi: 10.1007/s00415-005-0853-5
5 Ryan L , Lam C , Mataraso S , et al. Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study[J]. Ann Med Surg (Lond), 2020, 59, 207- 216.
doi: 10.1016/j.amsu.2020.09.044
6 Nemati S , Holder A , Razmi F , et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU[J]. Crit Care Med, 2018, 46 (4): 547- 553.
doi: 10.1097/CCM.0000000000002936
7 LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521 (7553): 436- 444.
doi: 10.1038/nature14539
8 Cheng JZ , Ni D , Chou YH , et al. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans[J]. Sci Rep, 2016, 6, 24454.
doi: 10.1038/srep24454
9 Kooi T , Litjens G , van Ginneken B , et al. Large scale deep learning for computer aided detection of mammographic lesions[J]. Med Image Anal, 2017, 35, 303- 312.
doi: 10.1016/j.media.2016.07.007
10 Choi E , Bahadori MT , Schuetz A , et al. Doctor AI: Predicting clinical events via recurrent neural networks[J]. JMLR Workshop Conf Proc, 2016, 56, 301- 318.
11 Hochreiter S , Schmidhuber J . Long short-term memory[J]. Neural Comput, 1997, 9 (8): 1735- 1780.
doi: 10.1162/neco.1997.9.8.1735
12 Thorsen-Meyer HC , Nielsen AB , Nielsen AP , et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: A retrospective study of high-frequency data in electronic patient records[J]. Lancet Digit Health, 2020, 2 (4): e179- e191.
doi: 10.1016/S2589-7500(20)30018-2
13 Xia J , Pan S , Zhu M , et al. A long short-term memory ensemble approach for improving the outcome prediction in intensive care unit[J]. Comput Math Methods Med, 2019, 2019, 8152713.
14 Maheshwari S , Agarwal A , Shukla A , et al. A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: Patients with cardiovascular disease[J]. Biomed Tech (Berl), 2020, 65 (4): 435- 446.
doi: 10.1515/bmt-2018-0206
15 Ho LV , Aczon M , Ledbetter D , et al. Interpreting a recurrent neural network's predictions of ICU mortality risk[J]. J Biomed Inform, 2021, 114, 103672.
doi: 10.1016/j.jbi.2021.103672
16 王琦琦, 于石成, 亓晓, 等. Logistic族回归及其应用[J]. 中华预防医学杂志, 2019, 53 (9): 955- 960.
17 Jhou HJ , Chen PH , Yang LY , et al. Plasma anion gap and risk of in-hospital mortality in patients with acute ischemic stroke: Analysis from the MIMIC-Ⅳ database[J]. J Pers Med, 2021, 11 (10): 1004.
doi: 10.3390/jpm11101004
18 Zhao N , Hu W , Wu Z , et al. The red blood cell distribution width-albumin ratio: A promising predictor of mortality in stroke patients[J]. Int J Gen Med, 2021, 14, 3737- 3747.
doi: 10.2147/IJGM.S322441
19 邱锡鹏. 神经网络与深度学习[M]. 北京: 机械工业出版社, 2020: 141- 145.
20 Kaji DA , Zech JR , Kim JS , et al. An attention based deep lear-ning model of clinical events in the intensive care unit[J]. PLoS One, 2019, 14 (2): e0211057.
doi: 10.1371/journal.pone.0211057
21 Lopez Bernal J , Soumerai S , Gasparrini A . A methodological framework for model selection in interrupted time series studies[J]. J Clin Epidemiol, 2018, 103, 82- 91.
doi: 10.1016/j.jclinepi.2018.05.026
22 Yu Y , Si X , Hu C , et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Comput, 2019, 31 (7): 1235- 1270.
doi: 10.1162/neco_a_01199
23 Gandin I , Scagnetto A , Romani S , et al. Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to intensive care unit[J]. J Biomed Inform, 2021, 121, 103876.
doi: 10.1016/j.jbi.2021.103876
24 Weimar C , Ziegler A , Konig IR , et al. Predicting functional outcome and survival after acute ischemic stroke[J]. J Neurol, 2002, 249 (7): 888- 895.
doi: 10.1007/s00415-002-0755-8
25 Koyama T , Uchiyama Y , Domen K . Outcome in stroke patients is associated with age and fractional anisotropy in the cerebral peduncles: A multivariate regression study[J]. Prog Rehabil Med, 2020, 5, 20200006.
26 Duarte E , Marco E , Muniesa JM , et al. Early detection of non-ambulatory survivors six months after stroke[J]. NeuroRehabilitation, 2010, 26 (4): 317- 323.
doi: 10.3233/NRE-2010-0568
27 Fuentes B , Castillo J , San Jose B , et al. The prognostic value of capillary glucose levels in acute stroke[J]. Stroke, 2009, 40 (2): 562- 568.
doi: 10.1161/STROKEAHA.108.519926
28 Baird TA , Parsons MW , Phanh T , et al. Persistent poststroke hyperglycemia is independently associated with infarct expansion and worse clinical outcome[J]. Stroke, 2003, 34 (9): 2208- 2214.
doi: 10.1161/01.STR.0000085087.41330.FF
29 Förstermann U , Münzel T . Endothelial nitric oxide synthase in vascular disease: From marvel to menace[J]. Circulation, 2006, 113 (13): 1708- 1714.
doi: 10.1161/CIRCULATIONAHA.105.602532
30 Virley D , Hadingham SJ , Roberts JC , et al. A new primate model of focal stroke: Endothelin-1-induced middle cerebral artery occlusion and reperfusion in the common marmoset[J]. J Cereb Blood Flow Metab, 2004, 24 (1): 24- 41.
doi: 10.1097/01.WCB.0000095801.98378.4A
31 Martini SR , Kent TA . Hyperglycemia in acute ischemic stroke: A vascular perspective[J]. J Cereb Blood Flow Metab, 2007, 27 (3): 435- 451.
doi: 10.1038/sj.jcbfm.9600355
32 You S , Zheng D , Zhong C , et al. Prognostic significance of blood urea nitrogen in acute ischemic stroke[J]. Circ J, 2018, 82 (2): 572- 578.
doi: 10.1253/circj.CJ-17-0485
33 Cheng J , Sun J , Yao K , et al. A variable selection method based on mutual information and variance inflation factor[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2022, 268, 120652.
doi: 10.1016/j.saa.2021.120652
34 Ge W , Huh JW , Park YR , et al. An interpretable ICU mortality prediction model based on Logistic regression and recurrent neural networks with LSTM units[J]. AMIA Annu Symp Proc, 2018, 2018, 460- 469.
35 Koppe G , Meyer-Lindenberg A , Durstewitz D . Deep learning for small and big data in psychiatry[J]. Neuropsychopharmacology, 2021, 46 (1): 176- 190.
doi: 10.1038/s41386-020-0767-z
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