Use of deep learning techniques in postoperative cardiac surgery care

  • The Lancet Pneumology

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Findings from a electronic health record data from a German tertiary care centre for cardiovascular diseases of adult patients

Findings from a electronic health record data from a German tertiary care centre for cardiovascular diseases of adult patients

Background

The large amount of clinical signals in intensive care units can easily overwhelm health-care personnel and can lead to treatment delays, suboptimal care, or clinical errors. The aim of this study was to apply deep machine learning methods to predict severe complications during critical care in real time after cardiothoracic surgery.

Methods

We used deep learning methods (recurrent neural networks) to predict several severe complications (mortality, renal failure with a need for renal replacement therapy, and postoperative bleeding leading to operative revision) in post cardiosurgical care in real time. Adult patients who underwent major open heart surgery from Jan 1, 2000, to Dec 31, 2016, in a German tertiary care centre for cardiovascular diseases formed the main derivation dataset. We measured the accuracy and timeliness of the deep learning model’s forecasts and compared predictive quality to that of established standard-of-care clinical reference tools (clinical rule for postoperative bleeding, Simplified Acute Physiology Score II for mortality, and the Kidney Disease: Improving Global Outcomes staging criteria for acute renal failure) using positive predictive value (PPV), negative predictive value, sensitivity, specificity, area under the curve (AUC), and the F1 measure (which computes a harmonic mean of sensitivity and PPV). Results were externally retrospectively validated with 5898 cases from the published MIMIC-III dataset.

Findings

Of 47 559 intensive care admissions (corresponding to 42 007 patients), we included 11 492 (corresponding to 9269 patients). The deep learning models yielded accurate predictions with the following PPV and sensitivity scores: PPV 0.90 and sensitivity 0.85 for mortality, 0.87 and 0.94 for renal failure, and 0.84 and 0.74 for bleeding. The predictions significantly outperformed the standard clinical reference tools, improving the absolute complication prediction AUC by 0.29 (95% CI 0.23–0.35) for bleeding, by 0.24 (0.19–0.29) for mortality, and by 0.24 (0.13–0.35) for renal failure (p<0.0001 for all three analyses). The deep learning methods showed accurate predictions immediately after patient admission to the intensive care unit. We also observed an increase in performance in our validation cohort when the machine learning approach was tested against clinical reference tools, with absolute improvements in AUC of 0.09 (95% CI 0.03–0.15; p=0.0026) for bleeding, of 0.18 (0.07–0.29; p=0.0013) for mortality, and of 0.25 (0.18–0.32; p<0.0001) for renal failure.

Interpretation

The observed improvements in prediction for all three investigated clinical outcomes have the potential to improve critical care. These findings are noteworthy in that they use routinely collected clinical data exclusively, without the need for any manual processing. The deep machine learning method showed AUC scores that significantly surpass those of clinical reference tools, especially soon after admission. Taken together, these properties are encouraging for prospective deployment in critical care settings to direct the staff’s attention towards patients who are most at risk.

Funding

No specific funding.

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