Sepsis 2 Onset and Mortality Among Adult Inpatients

Introduction

The early identification of sepsis cases is the difference between life and death for the patient, and is mission critical to healthcare providers re: quality and cost; also, this use case is well supported by Cerner Health Facts data (Note: 1) these data are deidentified and 2) we have more complete information for inpatients than outpatients). We focus on inpatients to cover a cohort with a much larger vulnerable patient population sample size, in an environment that may feature a smaller nurse/patient ratio.

Challenge Tasks and Data

The Challenge has three tasks:

1. Sepsis 2 onset risk prediction (4 hours before onset)
2. 30-day mortality risk prediction among sepsis patients (at the time of onset); and
3. Innovation regarding interpretability

We included all hospitalized adult (at least 16 years old) patients with suspicious infection. The sepsis 2 patients must meet at least 2 SIRS criteria:

1. Body temperature > 100.4 or < 95.0
2. RR > 20 or PaCO2 < 32mmHg
3. HR > 90/min
4. WBC > 12k or < 4k or Band > 10%

We excluded patients who 1) are children, and 2) have been in the hospital for less than 8 hours or more than 30 days.

There are 3 critical time points for each patient:

We will provide patient demographic and admission data for both tasks.

Task 1: Sepsis 2 Onset Risk Prediction (4 hours before onset)

Goal: To predict sepsis-2 onset 4 hours before it occurs

We provide clinical events and lab test results between T_admission

and T_onset - 4 for each patient, in the matrix format. The time is offset by T_admission.

Each patent is labeled with whether they have been identified for sepsis 2 onset.

Total data size:

Evaluation: Standard AUC, with randomly supplied samples from the testing cohort. We will test:

1. Case and control segments from the same patient: over the longer term ( > 4 hours before sepsis onset) vs. segmentation close to sepsis onset ( = 4 hours)
2. Case and control segments from different patients who have sepsis onset in the next 4 hours, as well as those who do not have a sepsis

Task 2: 30-day Mortality Risk Prediction for Patients Identified with Sepsis 2

Goal: To predict whether the patient will die in the hospital within 30 days, using up to 48 hours of data before sepsis onset.

We provide the clinical events and lab test results between T_onset - 48 and T_onset - 4 for each sepsis 2 patient, in the matrix format. The time is offset by T_onset.

Each patent is labeled with their mortality status and the time between T_onset and T_discharge.

Total data size:

Evaluation:

Cumulative case/dynamic control ROC; judge performance on multiple timestamps to see how well and how early (relative to mortality/discharge) the model M_i can obtain a good prediction from t_0nset

Evaluate and compare using R Package timeROC
https://cran.r-project.org/web/packages/timeROC/index.html

sensitivity^C(c,t) = P(M_i > c|T_i < t)
specificity^D(c,t) = P(M_i < c|T_i > t)

Using different time cutoffs t to calculate AUC (in the traditional way) allows one to access the model's performance in predicting short-term, medium term, and long term mortality risk after sepsis onset.

Task 3: Innovation Regarding Interpretability

While many machine learning models can conduct classification and regression tasks, not all of them achieve valid interpretation that potentially enables the application of findings to better inform decision support in the clinical setting.

There is no means of providing interpretability (e.g., automatic decisions on the threshold, finding combined patterns, designing novel visualizations, etc.), without evaluation from human experts. We have assembled a group of machine learning and clinical experts to judge the Challenge innovation track, which will be focused on interpretability.

Submitting Your Entry

The prediction result must be submitted via the SECURESTOR submission directory (one is assigned for each team).

For Task 1, please submit the probability that the patient will have sepsis 2 onset in the next 4 hours
For Task 2, please submit the probability for the patient’s mortality within 30 days

The submission must be in CSV (comma-separated) format, with column headers. Below is the sample layout for both tasks 1 and 2.

Rules

Rules:

1. Participants must not download the dataset
2. Participants are responsible for any additional access/logons created on their server and for keeping their password secret
3. Solutions must be submitted in the required format by the designated deadline